Challenges in Building Intelligent Open-domain Dialog Systems

05/13/2019 ∙ by Minlie Huang, et al. ∙ Microsoft Tsinghua University 5

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.

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1. Introduction

Building intelligent open-domain dialog systems that can converse with humans coherently and engagingly has been a long-standing goal of artificial intelligence (AI). Early dialog systems such as Eliza (Weizenbaum, 1966), Parry (Colby et al., 1971), and Alice (Wallace, 2009), despite being instrumental to significantly advancing machine intelligence, worked well only in constrained environments. An open-domain social bot remains an elusive goal until recently. The Microsoft XiaoIce (‘Little Ice’ literally in Chinese) system, since its release in May, 2014, has attracted millions of users and can converse with users on a wide variety of topics for hours (Zhou et al., 2018a; Shum et al., 2018). In 2016, the Alexa Prize challenge was proposed to advance the research and development of social bots that are able to converse coherently and engagingly with humans on popular topics such as sports, politics, and entertainment, for at least 20 minutes (Ram et al., 2018) 111Even though the dialog systems in this challenge are very complicated, they are more informational systems where user emotion need is less considered.

. The evaluation metric, inspired by the Turing Test

(Turing, 1950), is designed to test the social bots’ capacity of delivering coherent, relevant, interesting, free-form conversations and keeping users engaged as long as possible. However, the general intelligence demonstrated by these systems is still far behind humans. Building open-domain dialog systems that can converse on various topics like humans remains extremely challenging (Gao et al., 2019a).

In this paper we focus our discussion on three challenges in developing neural-based open-domain dialog systems, namely semantics, consistency and interactiveness. The rest of the paper is structured as follows. In the rest of Section 1, we compare open-domain dialog bots with traditional task-oriented bots and elaborate the three challenges. In Section 2, we survey three typical approaches to building neural-based open-domain dialog systems, namely, retrieval-based, generation-based, and hybrid methods. In Sections 3 to 5, we review the approaches that have been proposed to address the three challenges, respectively. In Section 6, we discuss dialog evaluation. We conclude the paper by presenting several future research trends in Section 7.

1.1. Open-Domain Dialog vs. Task-Oriented Dialog

Generally speaking, there are two types of dialog systems: one is task-oriented and the other is for open-domain dialog. Task-oriented dialog systems are designed for very specific domains or tasks, such as flight booking, hotel reservation, customer service, and technical support, and have been successfully applied in some real-world applications. Open-domain dialog systems, however, are much more challenging to develop due to its open-ended goal.

As outlined by Gao et al. (2019a), although both task-oriented dialog and open-domain dialog can be formulated as an optimal decision making process with the goal of maximizing expected reward, the reward in the former is better-defined and much easier to optimize than the latter. Consider a ticket-booking bot. It is straightforward to optimize the bot to get all necessary information to have the ticket booked in minimal dialog turns. The goal of an open-domain dialog agent is to maximize the long-term user engagement. This is difficult to optimize mathematically because there are many different ways (known as dialog skills) to improve the engagement (e.g., making entertainment, giving recommendation, chatting on an interesting topic, giving emotional comforting) and it requires the systems to have a deep understanding of dialog context and user’s emotional needs to select the right skill at the right time, and generate interpersonal responses with a consistent personality.

Open-domain dialog systems also differ from task-oriented bots in system architecture. A task-oriented bot is typically developed based on a pre-defined task-specific scheme222A task scheme typically defines a set of user intents, and for each intent defines a set of dialog acts, slot-value pairs. and is designed as a modular system which consists of domain-specific components like language understanding, dialog management333Dialog management performs both dialog state tracking (Henderson et al., 2013; Mrksic et al., 2017) and response selection via policy (Zhao and Eskénazi, 2016; Peng et al., 2017; Su et al., 2016; Lipton et al., 2018). , and language generation444Recently, there are end-to-end methods (Rojas-Barahona et al., 2017; Bordes et al., 2017; Zhang et al., 2019a) that output a response given the previous dialog history, but in general, domain knowledge about the task should be explicitly considered, which differs significantly from open-domain dialog systems.

. These components can be either hand-crafted based on domain knowledge or trained on task-specific labeled data. In comparison, due to the open-ended nature, open-domain dialog systems need to deal with open-domain knowledge without any pre-defined task-specific schemas or labels. In recent years, there has been a trend towards developing fully data-driven unitary (non-modular) systems that map user input to system response using neural networks. Since the primary goal of open-domain dialog bots is to be AI companions to humans with an emotional connection rather than completing specific tasks, they are often developed to mimic human conversations by training neural response generation models on large amounts of human conversational data

(Sordoni et al., 2015; Vinyals and Le, 2015; Shang et al., 2015).

Unlike task-oriented bots, most neural response generation models developed for open-domain dialog systems are not grounded in real world, which prevent these systems from effectively conversing about anything that relates to the user’s environment. Only recently have researchers begun to explore how to ground open-domain dialog systems in real-world entities and knowledge (Ghazvininejad et al., 2018; Mostafazadeh et al., 2017). Knowledge grounding is also crucial for the system to provide interpersonal responses. For instance, the conversations between friends are quite different from those between strangers. So the system needs to ground in the personas of the speaker and addressee, respectively (Li et al., 2016b). The tone of system responses needs to be adjusted according to user’s emotional states and affects. So the system needs to ground in affect or emotion of the user (Huber et al., 2018; Winata et al., 2017; Xu et al., 2018a).

Figure 1. Key issues (the middle block), key factors (the upper block), and key technologies (the bottom block) in open-domain dialog systems.

1.2. Three Challenges

Thanks to the recent success of neural approaches to natural language process and conversational AI, there have been substantial advancements on open-domain dialog systems. Most neural approaches formulate conversation as a task of generating output response given user input and dialog context. Almost all neural response generation models follow the neural text generation frameworks, such as sequence-to-sequence (Seq2Seq)

(Sutskever et al., 2014; Vinyals and Le, 2015; Shang et al., 2015)

, conditional variational autoencoder (CVAE)

(Sohn et al., 2015), and generative adversarial network (GAN) (Li et al., 2017). These models are trained on large-scale conversational data, and can deliver plausible responses to any topic that users are interested in. However, these neural-based systems often fail to establish long-term emotional connections with users due to many issues as illustrated by the examples in Table 1 and summarized in Figure 1. In what follows, we elaborate three key issues which we will discuss in the rest of the paper.

Semantics Issue
User I come from Shanghai.
Machine So cool, I am from Beijing, the same province!
User Back to Beijing now. So cold so happy!
Machine When will you come to Beijing?
Consistency Issue
User Are a boy or a girl?
Machine I am a boy.
User Are you a girl?
Machine Yes, I am a girl.
Interactiveness Issue
User Terrible breakfast today.
Machine How about together tomorrow morning?
User Sure. Would be great with your recommendation.
Machine Really?
Table 1. Examples for demonstrating the semantics, consistency, and interactiveness issues. The first two examples show semantic conflicts between user post and machine response, the third session shows bad consistency within the dialog context due to the lack of a coherent personality, and the last session has bad interactiveness due to the lack of grounding. The results in the first two blocks are from a standard Seq2Seq model with an attention mechanism, and the last session is from a commercial system.

Semantics

Semantics is the heart of any dialog system. Conversation is a semantic activity (Zhou et al., 2018e). It requires not only to understand the content, context, and scene of the conversation, but also to process multi-modal information including user’s personality and persona555Personality is someone’s character or nature while a persona is a superficial identity of the character or nature.

, emotion and sentiment, and profile and background. From the technical perspective, semantics mainly involves named entity recognition, entity linking, domain detection, topic and intent detection, sentiment and emotion detection, and knowledge and semantic reasoning.

Consistency

In order to gain user’s long-term confidence and trust, it is crucial for a dialog system to present a consistent personality and respond consistently given user’s input and dialog history (Li et al., 2016b; Qian et al., 2018; Zheng et al., 2019; Zhou et al., 2018a), For instance, a social bot should not deliver a response that conflicts with her pre-set persona, or her previous responses in temporal dependency, causality, or logic. From the technical perspective, consistency mainly involves personalization, multi-turn context modeling, knowledge grounding, and dialog planning.

Interactiveness

As mentioned above, meeting user’s social needs, such as emotional affection and social belonging, is the primary design goal of an open-domain dialog system. Interactiveness refers to the system’s ability to generate interpersonal responses to achieve a particular social goal such as entertainment, conforming, and task completion. To improve interactiveness, it is important to understand the user’s emotion state or affect (Zhou et al., 2018b, a), to respond not only reactively but also proactively (Yu et al., 2016; Wang et al., 2018b), to control the topic maintenance or transition (Wang et al., 2018a), and to optimize the interaction strategy (i.e., dialog policy) in multi-turn conversations to maximize long-term user engagement. From the technical perspective, interactiveness mainly involves sentiment and emotion detection, context modeling, topic detection and recommendation, dialog planning, and dialog policy learning.

2. Frameworks for Building Open-domain Dialog Systems

As discussed in Section 1.1, open-domain dialog systems are typically implemented using an unitary architecture, rather than a modular architecture used by task-oriented bots for which task-specific schemes and labels are available to develop dialog modules. At the heart of an open-domain dialog system is a response generation engine, which takes user input at -th dialog turn and dialog context , which will be explained in a minute, and generates response by

(1)

where denotes the set of all candidate responses, is a learned model of scoring candidate responses, parameterized by , and argmax the search algorithm to find among all candidates the best one with the highest score.

This formulation unifies three typical methods of building open-domain dialog systems: retrieval-based, generation-based, and hybrid. In retrieval-based methods, the search space is obtained by retrieving candidate responses from a pre-collected human conversational dataset consisting of input-context-response pairs. is implemented as a matching or ranking function which scores the relevance of each candidate given and . In generation-based methods, the search space is very large, namely where is the vocabulary size and is the response length, and is typically implemented as an auto-regressive model that generates a sentence word by word. In the hybrid methods, it is typical to first retrieve prototype responses from a dataset and then generates a response by utilizing prototype responses.

Note that the introduction of context offers a lot of flexibility to model various aspects of dialog. For instance, when , it models single-turn dialog; Setting models multi-turn dialogs. can also encode other contexts such as persona (Qian et al., 2018; Zhang et al., 2018b; Zheng et al., 2019) for personalized dialog generation, emotion labels (Zhou et al., 2018b; Asghar et al., 2018)

for emotional response generation, and knowledge graphs

(Zhou et al., 2018e; Ghazvininejad et al., 2018) for knowledge-aware response generation.

2.1. Retrieval-based Methods

Figure 2. Framework of retrieval-based methods. The online process finds the most relevant output from the retrieved candidate with a matching model while the offline process trains the matching model with the auto-constructed data.

Figure 2 illustrates the process of retrieval-based response generation methods. Using input 666Hereafter, we will use to denote the current input and the dialog context . as a query, such methods first retrieve a list of candidates from a large repository which consists of input-context-output pairs, and choose the top-scored candidate as output response using the matching function , which can be implemented using either traditional learning-to-rank algorithms (Liu, 2010), or modern neural matching models (Lu and Li, 2013; Huang et al., 2013; Fan et al., 2017). The model parameters is learned on pair-wise training data to minimize the margin-based pair-wise ranking loss as follows:

(2)

where is a margin (a hyper-parameter), is a ground-truth (positive) response, is a negative response which can be randomly sampled from the dataset or generated by corrupting , and is the matching function to be learned.

Alternatively, we can also use a likelihood loss defined as:

(3)

Although both loss functions are widely used, in our experiments we find the likelihood loss work better than the margin-based loss for response ranking. There are two possible interpretations. First, the hyper-parameter

is difficult to tune. Second, in the cases where there are highly competitive negative examples, the margin-based loss is close to zero, thereby leading to very little model update. The likelihood loss does not suffer from these issues.

Modern neural models of can be roughly grouped into two categories, shallow and deep interaction networks, as illustrated in Figure 3. In shallow interaction networks, candidate and input

are first encoded independently into the two vectors which then have some

shallow interactions such as subtraction or element-wise multiplication before being fed to the classification layer. In deep interaction networks, and interact via an interaction network to form a fused representation, which is then fed to the classification layer.

Figure 3. Frameworks of shallow and deep interaction networks. In shallow interaction network, the feature vectors of input and candidate are obtained independently, and there may be shallow interactions such as subtraction or element-wise multiplication between the two vectors before the classification layer. In deep interaction network, the input and candidate make interactions in the early stage to obtain a feature vector for the classification layer.

For shallow interaction networks, many efforts have been devoted to learning good representations for input and candidate independently. Huang et al. (2013) proposed to use deep structured similarity models (DSSMs) to extract semantic features from query and document independently before computing their relevance. DSSM is further augmented by introducing Convolutional layers (Shen et al., 2014; Hu et al., 2014; Severyn and Moschitti, 2015)

and recurrent layers with Long Short-Term Memory (LSTM) units

(Palangi et al., 2016). To effectively incorporate dialog history, Yan et al. (2016) reformulated input query , and combined matching scores computed based on the reformulated and original queries, and retrieved queries and responses, respectively. Zhou et al. (2016)

used a hierarchical Recurrent Neural Network (RNN) to encode a candidate and the utterance sequence in context, respectively, before computing their matching score. These shallow models are simple to implement and efficient to execute.

For deep interaction networks, query and response interact via a neural network to generate a single feature vector that preserves all query-response interaction information at different levels of abstraction. The matching score is then derived from the vector using another neural network. Hu et al. (2014) extracted matching features from all -gram combinations of input and response

to obtain low-level feature maps with a Convolutional Neural Network (CNN). Afterwards, the feature maps are transformed with multiple CNN layers to form the final representation for classification.

Wu et al. (2017) proposed a sequential matching model for multi-turn dialog where each contextual utterance in is encoded conditioned on , and these utterances are connected sequentially by GRUs. The matching score is computed on top of the weighted sum of the GRUs’ states. Other matching models that were proposed originally for non-dialog tasks such as paraphrase detection, language inference, and reading comprehension (Wang et al., 2017a; Pang et al., 2016), have also been adapted and applied to dialog response ranking.

2.2. Generation-based Methods

Neural generative models have been widely applied to open-domain dialog generation. Inspired by the early template-based generation method (Higashinaka et al., 2014) and statistical machine translation (SMT)  (Ritter et al., 2011), sequence-to-sequence (Seq2seq) models (Sutskever et al., 2014; Vinyals and Le, 2015; Shang et al., 2015; Sordoni et al., 2015) have become the most popular choice for dialog generation. Other frameworks, including conditional variational autoencoder (CVAE) (Serban et al., 2017; Zhao et al., 2017; Ke et al., 2018; Shen et al., 2018; Zhao et al., 2018; Du et al., 2018) and generative adversarial network (GAN) (Li et al., 2017; Xu et al., 2018b), are also applied to dialog generation.

Generation-based models usually formulate as below:

(4)

where . Typically, the output response is generated word by word, e.g., at each time step a word is sampled according to . Using RNNs, during the course of generation, the generated prefix is autoregressively encoded into the input to generate the next word.

Most neural generation models adopt an encoder-decoder framework. The encoder transforms the input into semantic vectors as

(5)

Then, at each -th step of generation, the decoder updates its state vector and samples a word from distribution as follows:

(6)

where is the weight matrix of the decoder. The decoder’s state is updated by

(7)

where is an attentive read of the encoded input conditioned on state , typically using attention mechanism (Bahdanau et al., 2015); and is the vector representation of the previously generated word .

The formulation of generation-based models mentioned above is auto-regressive in that these models generate a target sequence word by word, each word conditioned on the words that are previously generated. To make the decoding parallelizable, non-autoregressive models based on Transformer have been proposed to generate all the tokens simultaneously

(Kaiser et al., 2018; Lee et al., 2018). Non-autoregressive modeling factorizes the distribution over a target sequence given a query into a product of conditionally independent per-step distributions, as follows:

(8)

Though the performance of such non-autoregressive models is still not as good as their autoregressive counterparts, it opens new opportunities for fast training using very large scale datasets.

Figure 4. Typical encoder-decoder framework for generation-based models. The input is encoded into vectors . In the decoder, a word is sampled from and the decoder’s state is updated with and as input.

2.3. Hybrid Methods

Retrieval-based methods retrieve an output response from a repository of human-human conversations. Such human-produced conversations are fluent, grammatical, and of high quality. However, the scale of the repository is critical to the success of the methods. Moreover, retrieval-based methods cannot generate unseen conversations. On the other hand, generation-based methods can produce novel conversations. But they often generate undesirable responses that are either ungrammatical or irrelevant. Hybrid methods combine the strengths of both and usually adopt a two-stage procedure (Yang et al., 2019). In the first stage, some relevant conversations, known as prototype responses in (Wu et al., 2019), are retrieved from a dataset using input as a query. Then, prototype responses are used to help generate new responses in the second stage.

Based on the Seq2Seq architecture, Song et al. (2018) used additional encoders to represent the set of retrieved responses, and applied the attention (Bahdanau et al., 2015) and copy (Gu et al., 2016) mechanism in decoding to generate new responses. Pandey et al. (2018) first retrieved similar conversations from training data using a TF-IDF model. The retrieved responses were used to create exemplar vectors that were used by the decoder to generate a new response. Wu et al. (2019) first retrieved a prototype response from training data and then edited the prototype response according to the differences between the prototype context and current context. The motivation is that the retrieved prototype provides a good start-point for generation because it is grammatical and informative, and the post-editing process further improves the relevance and coherence of the prototype.

3. Semantics

A typical symptom of a dialog system that suffers from the semantics issue is that it often generates bland and generic responses, such as “I don’t know”, “thank you”, “OK” , or simply repeats whatever a user says  (Sordoni et al., 2015; Vinyals and Le, 2015; Serban et al., 2016; Gao et al., 2019a). We observe similar phenomena in human conversations. When we don’t understand what the other party is talking about but have to respond, we often pick those safe but bland responses like “OK” and “I don’t know”.

To make an engaging conversation, the dialog system needs to produce contentful, interesting, and interpersonal responses based on its understanding of the dialog content, user’s sentiment and emotion, and knowledge that is related to the dialog. In this section, we review some of the most prominent neural approaches that have been proposed recently to address the semantics issue. We first describe the ways of improving the encoder-decoder framework to generate diverse and informative responses. Then, we describe the methods of grounding dialog in real-world knowledge to make system responses more contentful.

3.1. Improving Diversity and Informativeness in Neural Response Generation

Most state of the art neural response generation models are based on the encoder-decoder framework which consists of four components: (1) an encoder that encodes user input and dialog context, (2) an intermediate representation, (3) an decoder that generates candidate responses, and (4) a ranker that picks the best candidate as the response. In what follows, we review the proposed methods in four categories, each focusing on improving one of the four components.

Encoder

Encoding more information from query , such as longer dialog history (Sordoni et al., 2015), persona (Li et al., 2016b), hidden topics (Serban et al., 2017), has proved to be helpful for generating more informative results. Xing et al. (2017) extracted topic words, rather than hidden topics, using LDA, and encoded such words in a topic-aware model. The model generates a response by jointly attending to input and the topic words. Topic words are also used to model topic transition in multi-turn conversations (Wang et al., 2018a). The hybrid methods described in Section 2.3 (Pandey et al., 2018; Song et al., 2018; Wu et al., 2019) encode the retrieved prototype responses to help generate more informative responses.

Intermediate Representation

Instead of encoding using a fixed size vector as in (Sutskever et al., 2014), methods have been proposed to use richer intermediate representations (e.g., by using additional latent variables) to enhance the representation capability to address the one-to-many issue in dialog, and to improve the interpretability of the representation in order to better control the response generation. Zhao et al. (2017)

introduced CVAE for dialogue generation and adopted a Gaussian distribution, rather than a fixed vector, as the form of representation, thus obtaining diverse responses via sampling the latent variable.

Du et al. (2018) introduced a sequence of continuous latent variables to model response diversity, and demonstrated empirically that it is more effective than using a single latent variable. Zhao et al. (2018) proposed an unsupervised representation learning method to use discrete latent variables, instead of dense continuous ones, which improves the interpretability of representation. Zhou et al. (2017, 2018c) assumed that there exist some latent responding mechanisms, each of which can generate different responses for a single input post. These responding mechanisms are modeled as latent embeddings, and can be used to encode the input into mechanism-aware context to generate responses with the controlled generation styles and topics. Gao et al. (2019b) proposed a SpaceFusion model which induces a latent space that fuses the two latent spaces generated by Seq2Seq and auto-encoder, respectively, in such a way that after encoding into a vector in the space, the distance and direction from the predicted response vector given the context roughly match the relevance and diversity, respectively.

Decoder

Assigning additional probability mass to

desirable words in decoder is a commonly used method to gain the control of what to generate. Mathematically, this can be implemented by adjusting the output word distribution as follows:

(9)

where is the generated prefix; assigns additional probabilities to the words to be controlled; and

is a normalization function to ensure a probablity distribution. Many existing works use this formulation. The most notable example is CopyNet

(Gu et al., 2016), which copies infrequent words from the input to the output, thus assigning higher probabilities to those rare words. In (Zhang et al., 2018c), is formulated as a Gaussian distribution, which assigns higher probabilities to rare words to control the specificity of a response, where the specificity score of a word is proportional to its IDF (inverse document frequency) score.

Candidate Ranker

To obtain more diverse responses, beam search is commonly used to generate multiple candidates, which are then ranked by another model, which uses information that is not available in decoding (e.g., mutual information between input and response) or is too expensive to use in decoding (e.g.,, a large pre-trained language model such as BERT (Devlin et al., 2019)) to pick the final response. Li et al. (2016a) proposed to use Maximum Mutual Information (MMI) as the objective to rank candidates to promote the diversity of generated responses. As the standard beam search often produces near-identical results, recent work improves it by encouraging the diversity among (partial) hypotheses in the beam. For example, Li et al. (2016c) penalized lower-ranked siblings extended from the same parents, so that the N-best hypotheses in the beam at each time step are more likely to expand from different parents, and thus more diverse. Vijayakumar et al. (2018) divided the hypotheses into several groups and applied beam search group by group. The model favours the hypotheses that are dissimilar to the ones in the previous groups.

3.2. Knowledge Grounded Dailog Models

Knowledge is crucial for language understanding and generation. To build effective human-machine interactions, it is indispensable to ground the concepts, entities, and relations in text to commonsense knowledge or real-world facts such as those stored in Freebase and Wikipedia. An open-domain dialog system, equipped with rich knowledge and knowledge grounding capability, should be able to identify the entities and topics mentioned in user input, link them into real-world facts, retrieve related background information, and thereby respond users in a proactive way e.g., by recommending new, related topics to discuss.

Knowledge has been shown useful in both retrieval-based and generation-based dialog systems. A well-known example of the former is Microsoft XiaoIce (Zhou et al., 2018a). XiaoIce relies on a large knowledge graph (KG) to identify the topics and knowledge related to user input for both response generation and topic management. In (Young et al., 2018), a Tri-LSTM model is proposed to use commonsense knowledge as external memories to facilitate the model to encode commonsense assertions for response selection. An early example of using knowledge for generating responses is (Han et al., 2015), where manually crafted templates are used to generate responses which are filled with relevant knowledge triples. In (Ghazvininejad et al., 2018), a knowledge-grounded model is proposed to generate a response by incorporating some retrieved posts that are relevant to the input. The knowledge in (Ghazvininejad et al., 2018) is in the form of unstructured posts retrieved by an information retrieval model, and the quality is mixed. Pre-compiled structured knowledge, which is in the form of fact triples, is believed to be of high quality and has been shown to help conversation generation (Zhu et al., 2017; Liu et al., 2018). Zhu et al. (2017) dealt with a scenario where two speakers are conversing based on each other’s private knowledge bases in the music domain. The generation model can generate a word in response from either the context or the knowledge base. In (Liu et al., 2018), a knowledge diffusion model is proposed to not only answer factoid questions based on a knowledge base, but also generate an appropriate response containing knowledge base entities that are relevant to the input. Zhou et al. (2018e) exploited the use of large-scale commonsense knowledge for conversation generation. First, a one-hop subgraph is retrieved from ConceptNet (Speer et al., 2017) for each word in an input post. Then, the word vectors, along with the graph vectors which extend the meaning of the word via its neighboring entities and relations, are used to encode the input post. During decoding, a graph attention mechanism is applied in which the model first attends to a knowledge graph and then to a triple within each graph, and the decoder chooses a word to generate from either the graph or the common vocabulary.

4. Consistency

A human-like dialog system needs to embody a consistent personality, so that it can gain the user’s confidence and trust (Shum et al., 2018; Zhou et al., 2018a). Personality settings include age, gender, language, speaking style, general (positive) attitude, level of knowledge, areas of expertise, and a proper voice accent. For example, the persona of XiaoIce (Zhou et al., 2018a) is designed as a 18-year-old girl who is always reliable, sympathetic, affectionate, and has a wonderful sense of humor. Despite being extremely knowledgeable (due to the access to large volumes of data), XiaoIce never comes across as egotistical and only demonstrates her wit and creativity when appropriate. However, modeling these factors in dialog systems remains very challenging because the embodiment of these personality features is often very implicit and subtle, especially when they have to be expressed using natural language.

The studies of personalization in dialog models can be roughly classified into two types:

implicit personalization and explicit personalization. In the former type, the user personalization is represented by a vector. For instance, Kim et al. (2014) proposed a ranking-based approach to integrate a personal knowledge base and user interests in dialogue system. Bang et al. (2015) extended the user input by exploiting examples retrieved from her personal knowledge base to help identify the candidate responses that fit her persona. Li et al. (2016b); Zhang et al. (2017) used an embedding vector to represent a user (speaker) persona and fed the user embedding into each decoding position of the decoder. Such models need to be trained using conversational data labeled by user identifiers, which is expensive to collect for large quantities. Thus, Wang et al. (2017c) proposed to train personalized models with only group attributes (e.g., male or female). The group attributes are embedded to vectors and then fed into the decoder for response generation. Although Zhang et al. (2018d); Ouchi and Tsuboi (2016) showed that user embedding is an effective technique to distinguish roles of speakers and addressees in multi-party conversation, personalization in these models are handled in an implicit way and thus not easy to interpret and control in generating desired responses. In (Qian et al., 2018), an explicit personalization model is proposed to generate personality-coherent responses given a pre-speficified profile. The chatbot’s personality is defined by a key-value table (i.e., profile) which consists of name, gender, age, hobbies, and so on. During generation, the model first chooses a key-value from the profile and then decodes a response from the chosen key-value pair forward and backward. This model can be trained on generic dialogue data without user identifier. XiaoIce also uses an explicit personalization model (Zhou et al., 2018a).

In the studies of (Zhang et al., 2017; Mo et al., 2018; Casanueva et al., 2015; Wang et al., 2017b)

, personalized conversation generation is cast as domain adaptation or transfer learning. The idea is to first train a general conversation model on a large corpus (source domain) and then to transfer the model to a new speaker or domain using small amounts of personalized data (target domain).

Casanueva et al. (2015) proposed to automatically gather dialogues from similar speakers to improve the performance of policy learning of personalized dialogue systems. Zhang et al. (2017) proposed a two-phase transfer learning approach, namely initialization then adaptation, to generate personalized responses. They also proposed a quasi-Turing test method to evaluate the performance of the generated personalized responses. Yang et al. (2017) presented a transfer learning framework similar to Zhang et al. (2017)

, but proposed to use a new adaptation mechanism based on reinforcement learning.

Luan et al. (2017) proposed a multi-task learning approach to take the response generation and utterance representation as two sub-tasks for speaker role adaptation.

Stylistic response generation (Wang et al., 2017b; Oraby et al., 2018) can be viewed as a form of personalization in conversation. The main challenges lie in two aspects: disentangling content and style in representation, and constructing parallel corpora containing same content with different styles. Wang et al. (2017b) utilized a small-scale stylistic data and proposed a topic embedding model to generate responses in specific styles and topics simultaneously. Oraby et al. (2018) demonstrated that it is possible to automate the construction of a parallel corpus where each meaning representation can be realized in different styles with controllable stylistic parameters.

There have been increasing efforts of building personalized dialogue corpora. In (Zhang et al., 2018b), a multi-turn dialogue corpus is constructed, where each dialogue session involves two speakers and the persona of each speaker is defined by several sentences describing the speaker’s hobbies or preferences. Mazaré et al. (2018) presented a simple method of constructing a large-scale personalized dataset from social media where user’s persona is defined by a set of sentences of particular patterns describing their preferences. Joshi et al. (2017) developed a personalized version of the bAbI dialoge dataset (Bordes et al., 2017) by associating each goal-oriented dialog with user traits such as gender, age, and favorite foods. In (Zheng et al., 2019), a large-scale personalized dialog corpus has been developed. The corpus consists of multi-turn conversations collected from Weibo with speaker IDs. Each speaker is associated with her personal information including gender, age, location, and interest.

5. Interactiveness

Interactiveness refers to the system’s ability to generate interpersonal responses to maximize long-term user engagement. To improve interactiveness, it is important to understand user’s emotion and affect, in addition to dialog content, and to optimize the system’s behavior and interaction strategy in multi-turn conversations.

5.1. Modeling User Emotion

Emotion perception and expression is vital for building a human-like dialog system. Earlier attempts of building emotional dialog systems are mostly inspired by psychology findings. Those systems are either rule-based or trained on small-scale data, and work well only in a controlled environment. Thanks to the availability of large-scale data and the recent progress on neural conversational AI, many neural response generation models have been proposed to perceive and express emotions in an open-domain dialog setting.  Zhou et al. (2018b) proposed Emotional Chatting Machine (ECM) to generate emotional responses given a pre-specified emotion. ECM consists of three components: (1) emotion category embedding which is fed into each decoding position, (2) internal emotion state which assumes that the emotion state decays gradually and finally to zero during decoding, and (3) external memory which allows the model to choose emotional (e.g., lovely) or generic (e.g., person) words explicitly at each decoding step. The authors also presented some typical emotion interaction patterns in human-human conversations such as empathy and comfort, which may inspire the design of emotion interaction between human and machine. Asghar et al. (2018) developed a method of affective response generation that consists of three components: (1) the affective vectors based on Valence/Arousal/Dominance dimensions (Warriner et al., 2013), which serve as a supplement to word vectors; (2) the affective loss functions which maximize or minimize the affective consistency between a post and a response; and (3) the affective beam search algorithm for seeking affective responses. In (Zhou and Wang, 2018), a conditional variational autoencoder is proposed to generate more emotional responses conditioned on an input post and some pre-specified emojis. Huber et al. (2018)

studied how emotion can be grounded in an image to generate more affective conversations. In addition to text, the decoder of the model also takes as input the scene, sentiment, and facial coding features extracted from a given image.

Controlling the emotion or sentiment has been a recent popular topic in language generation (Hu et al., 2017; Radford et al., 2017; Ghosh et al., 2017). In (Radford et al., 2017)

, an RNN-based language model is trained on large-scale review data where some neurons are reported to be highly correlated with sentiment expression.

Ghosh et al. (2017)

proposed an affective language model which generates an affective sequence from a leading context. At each decoding position, the model estimates an affective vector of the already generated prefix by keyword spotting using the Linguistic Inquiry and Word Count (LIWC) dictionary

(Pennebaker et al., 2001). The vector is then used to generate the next word. In (Wang and Wan, 2018), to generate reviews of a particular polarity, the authors proposed a multi-class generative adversarial network which consists of multiple generators for multi-class polarities and a multi-class discriminator.

5.2. Modeling Conversation Behavior and Strategy

As pointed out in (Zhou et al., 2018a), an open-domain dialog system needs to have enough social skills to have engaging conversations with users and eventually establish long-term emotional connections with users. These social skills include topic planning and dialog policy which can determine whether to drive the conversation to a new topic when e.g., the conversation has stalled, or whether or not to be actively listening when the user herself is engaged in the conversation. Nothdurft et al. (2015) elucidated the challenges of proactiveness in dialogue systems and how they influence the effectiveness of turn-taking behaviour in multimodal and unimodal dialogue systems. Yu et al. (2016) proposed several generic conversational strategies to handle possible system breakdowns in non-task-oriented dialog systems, and designed policies to select these strategies according to dialog context. Zhang et al. (2018a) proposed a task of predicting from the very beginning of a conversation whether it will get out of hand. The authors developed a framework for capturing pragmatic devices, such as politeness strategies and rhetorical prompts, used to start a conversation, and analyzed their relation to its future trajectory. Applying this framework in a controlled setting, it is possible to detect early warning signs of antisocial behavior in online discussions.

The above studies inspire researchers to devise new methods of incorporating social skills into an open-domain dialog system. In (Li et al., 2016d), a retrieval-based method is proposed to first detect the sign of stalemate using rules, and then retrieve responses that contain the entities that are relevant to the input, assuming that a proactive reply should contain the entities that can be triggered from the ones in the input. Yan and Zhao (2018) proposed a proactive suggestion method where a look-ahead post for a user is decoded in addition to the system response, conditioned on the context and the previously generated response. The user can use the generated post directly, or type a new one during conversation. Wang et al. (2018b) argued that asking good questions in conversation is shown to be an important proactive behavior. A typed decoder is proposed to generate meaningful questions by predicting a type distribution over topic words, interrogatives, and ordinary words at each decoding position. The final output distribution is modeled by the type distribution, leading to a strong control over the question to be generated. Ke et al. (2018) conducted a systematic study of generating responses with different sentence functions, such as interrogative, imperative, and declarative sentences. These sentence functions play different roles in conversations. For instance, imperative responses are used to make requests, give directions and instructions, or elicit further interactions; and declarative responses make statements or explanations.

6. Dialog Evaluation

Evaluating the quality of an open-domain dialog system is challenging because open-domain conversations are inherently open-ended (Ram et al., 2018). For example, if a user asks the question ”what do you think of Michael Jackson?”, there are hundreds of distinct but plausible responses. Evaluation of a dialog system can be performed manually or in an automatic way. In manual evaluation, human judges are hired to assess the generated results in terms of predefined metrics, with well-documented guidelines and exemplars. Evaluation is conducted by either scoring each individual result (point-wise) or comparing two competing results (pair-wise). In some dialog evaluation challenges, manual evaluation is commonly adopted in the final-stage competition  (Dinan et al., 2019; Ram et al., 2018). For instance, the second conversational intelligence challenge (Dinan et al., 2019) adopted manual evaluation by paid workers from Amazon Mechanical Turk and unpaid volunteers, and the organizers reported the rating difference between the two user groups: the volunteers’ evaluation had relatively fewer good (i.e. long and consistent) dialogues, while paid workers tended to rate the models higher than the volunteers.

Since manual evaluation is expensive, time-consuming, and not always reproducible, automatic evaluation is more frequently used, especially at the early stage of development. For retrieval-based methods, traditional information retrieval evaluation metrics such as precision@k, mean average precision (MAP), and normalized Discounted Cumulative Gain (nDCG) 

(Manning et al., 2008) are applicable. For generation-based models, metrics such as perplexity, BLEU (Papineni et al., 2002), and distinct- (Li et al., 2016a), are widely used. Perplexity measures how well a probabilistic model fits the data, and is a strong indicator whether the generated text is grammatical. BLEU, adopted from machine translation, measures the lexical overlap between the generated responses and the reference ones. Distinct- measures the diversity by computing the proportion of unique -grams in a generated set. However, (Liu et al., 2016) argued that automatic metrics such as BLEU, ROUGE (Lin, 2004), and METEOR (Banerjee and Lavie, 2005) all have low correlation with manual evaluation. But as pointed out in (Gao et al., 2019a), the correlation analysis in (Liu et al., 2016) is performed at the sentence level while BLEU is designed from the outset to be used as a corpus-level metric. (Galley et al., 2015) showed that the correlation of string-based metrics (BLEU and deltaBLEU) significantly increases with the units of measurement bigger than a sentence. Nevertheless, in open-domain dialog systems, the same input may have many plausible responses that differ in topics or contents significantly. Therefore, low BLEU (or other metrics) scores do not necessarily indicate low quality as the number of reference responses is always limited in test set. Therefore, there has been significant debate as to whether such automatic metrics are appropriate for evaluating open-domain dialog systems (Gao et al., 2019a).

Recently, trainable metrics for dialog evaluation have attracted some research efforts. Lowe et al.  (2017)

proposed a machine-learned metric, called ADEM, for dialog evaluation. They presented a variant of the VHRED model

(Serban et al., 2017) that takes context, user input, gold and system responses as input, and produces a qualitative score between 1 and 5. The authors claimed that the learned metric correlates better with human evaluation than BLEU and ROUGE. (Tao et al., 2018) proposed an evaluation model, called RUBER, which does not rely on human judged scores. RUBER consists of a referenced component to measure the overlap between a system response and a reference response, and an unreferenced component to measure the correlation between the system response and the input utterance. However, as pointed out in (Sai et al., 2019), ADEM can be easily fooled with a variation as simple as reversing the word order in the text. Their experiments on several such adversarial scenarios draw out counter-intuitive scores on the dialogue responses. In fact, any trainable metrics lead to potential problems such as overfitting and “gaming of the metric” 777In discussing the potential pitfalls of machine-learned evaluation metrics, Albrecht and Hwa (2007)

argued for example that it would be “prudent to defend against the potential of a system gaming a subset of the features.” In the case of deep learning, this gaming would be reminiscent of making non-random perturbations to an input to drastically change the network’s predictions, as it was done, e.g., with images in

(Szegedy et al., 2013) to show how easily deep learning models can be fooled. Readers refer to Chapter 5 in Gao et al. (2019a) for a detailed discussion.
(Albrecht and Hwa, 2007), which might explain why none of the previously proposed machine-learned evaluation metrics (Corston-Oliver et al., 2001; Kulesza and Shieber, 2004; Lita et al., 2005; Albrecht and Hwa, 2007; Giménez and Màrquez, 2008; Pado et al., 2009; Stanojević and Sima’an, 2014, etc.) is used in official machine translation benchmarks. Readers refer to (Gao et al., 2019a) for a detailed discussion.

All of this suggests that automatic evaluation of dialog systems is by no means a solved problem. We believe that developing a successful automatic evaluation metric has two prerequisites. First, there should be a fairly large, representative conversational dataset. This dataset should have a good coverage of daily life topics and domains. Second, for each input, there should be multiple appropriate responses to address the one-to-many essence in open-domain dialog.

7. Discussions and Future Trends

In this paper, we review the recent progress in developing open-domain dialog systems. We focus the discussion on neural approaches that have been proposed to deal with three key challenges: semantics, consistency, and interactiveness. We also review dialog evaluation metrics for both manual and automatic evaluation, and share our thoughts on how to develop automatic evaluation metrics.

Differing from early generations of dialog assistants which are designed for the tasks that require only short, domain-specific conversations, such as making reservation or asking for information, open-domain dialog systems are design to be AI companions that are able to have long, free-form social chats that occur naturally in social and professional human interactions (Ram et al., 2018; Zhou et al., 2018a). Despite the recent progress as reviewed in this paper, achieving sustained, coherent, and engaging open-domain conversations remains very challenging. Here, we discuss some future trends that may contribute to building more intelligent open-domain dialog systems:

Topic and Knowledge Grounding

To deliver contentful conversations, it is important to ground conversations in real-world topics or entities (e.g., in knowledge bases). This is part of the semantics challenge we discussed in Section 3. Since natural language understanding in open domains is extremely challenging, knowledge grounding provides to some degree the ability of understanding language in dialog context, as shown in several preliminary studies (Zhou et al., 2018e; Liu et al., 2018; Zhu et al., 2017). Even though an open-domain dialog system has no access to annotated dialog acts (which are available only for task-oriented dialog) to learn to explicitly detect user intents (labeled by dialog acts), the system can still play a proactive role of leading the conversation by for example suggesting new topics or providing new information, if the key concepts and entities are correctly recognized and linked to a knowledge base (Fang et al., 2018; Pichl et al., 2018; Wang et al., 2018b; Zhou et al., 2018a). Some recently proposed corpora, such as Document-grounded conversation (Zhou et al., 2018d) and Wizard of Wikipedia (Dinan et al., 2018), which aim to build a chatbot for delivering conversations grounded in the topics of a document or a Wikipedia page, respectively, provide new test beds for this research.

Empathetic Computing

Sentiment/emotion is a key factor for making effective social interactions, and is crucial for building an empathetic social bot to improve interactiveness. Existing works (Zhou et al., 2018b; Asghar et al., 2018; Zhou et al., 2018a; Zhou and Wang, 2018) in this direction are still in the infant stage, as they only deal with superficial expression of emotion. In the future, an empathetic machine should be able to perceive a user’s emotion state and change, deliver emotionally influential conversations, and evaluate the emotional impact of its action, much of which should be tightly aligned to psychological studies. These become more crucial in more complicated scenarios such as psychological treatment, mental health, and emotional comforting. Moreover, it is insufficient for an empathetic machine to use only text information. The signals from other modalities such as facial expression and speech prosody should also be leveraged (Liao et al., 2018; Zhang et al., 2019b). To foster the research, Saha et al. (2017) developed a conversational dataset consisting of multi-modal dialog sessions in a fashion domain where each turn contains a textual utterance, one or more images, or a mix of text and images.

Personality of a Social Bot

A coherent personality is important for a social bot to gain human trust, thereby improving the consistency and interactiveness of human-machine conversations. Personality (e.g., Big five traits) has been well defined in psychology (Norman, 1963; Gosling et al., 2003). However, existing works (Li et al., 2016b; Qian et al., 2018; Zhang et al., 2018b; Zhou et al., 2018a) are still very preliminary, and need to be significantly extended by incorporating the results of multidiscipline research covering psychology, cognitive science, computer science, etc. The central problem is how to ensure personality-coherent behaviors in conversations and evaluate such behaviors from the perspectives of multidisciplines, particularly via psychological studies.

Controllability of dialog generation

Most existing open domain dialog systems depend on neural dialog generation models. Due to the essence of probabilistic sampling used in language generation, controllability is a big issue as repetitive, bland, illogical or even unethical responses are frequently observed. Controllability is closely related to the interpretability and robustness of neural network models, and solving it requires new methods, such as the hybrid approaches that combine the strengths of both neural and symbolic methods.

8. Acknowledgement

This work was supported by the National Key R&D Program of China (Grant No. 2018YFC0830200), and partly by the National Science Foundation of China (Grant No.61876096/61332007).

We would like to thank Pei Ke, Qi Zhu, Yilin Niu, Zhihong Shao, Yaoqin Zhang, Hao Zhou, Chris Brockett, Bill Dolan, and Michel Galley for their discussions and contributions to this paper.

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