Identifying, classifying, and talking about objects and events in the surrounding environment are key capabilities for intelligent, goal-driven systems that interact with other humans and the external world (e.g. robots, smart spaces, and other automated systems). To this end, there has recently been a surge of interest and significant progress made on a variety of related tasks, including generation of Natural Language (NL) descriptions of images, or identifying images based on NL descriptions[Bruni et al.2014, Socher et al.2014, Farhadi et al.2009, Silberer and Lapata2014, Sun et al.2013]. Another strand of work has focused on incremental reference resolution in a model where word meaning is modeled as classifiers (the so-called Words-As-Classifiers model [Kennington and Schlangen2015]).
|(a) Dialogue Example from the corpus|
|(b) The Chat Tool Window during dialogue in (a) above|
However, none of this prior work focuses on how concepts/word meanings are learned and adapted in interactive dialogue with a human, the most common setting in which robots, home automation devices, smart spaces etc. operate, and, indeed the richest resource that such devices could exploit for adaptation over time to the idiosyncrasies of the language used by their users.
), it has made use of hand-constructed, synthetic dialogue examples that thus lack in variation, and many of the characteristic, but consequential phenomena observed in naturalistic dialogue (see below). Indeed, to our knowledge, there is no existing data set of real human-human dialogues in this domain, suitable for training multi-modal conversational agents that perform the task ofactively learning visual concepts from a human partner in natural, spontaneous dialogue.
|(a) Multiple Dialogue Actions in one turn|
Natural, spontaneous dialogue is inherently incremental [Crocker et al.2000, Ferreira1996, Purver et al.2009], and thus gives rise to dialogue phenomena such as self- and other-corrections, continuations, unfinished sentences, interruptions and overlaps, hedges, pauses and fillers. These phenomena are interactionally and semantically consequential, and contribute directly to how dialogue partners coordinate their actions and the emergent semantic content of their conversation. They also strongly mediate how a conversational agent might adapt to their partner over time. For example, self-interruption, and subsequent self-correction (see example in table 1.b) as well as hesitations/fillers (see example in table 1.e) aren’t simply noise and are used by listeners to guide linguistic processing [Clark and Fox Tree2002]; similarly, while simultaneous speech is the bane of dialogue system designers, interruptions and subsequent continuations (see examples in table 1.c and 1.d) are performed deliberately by speakers to demonstrate strong levels of understanding [Clark1996].
Despite this importance, these phenomena are excluded in many dialogue corpora, and glossed over/removed by state of the art speech recognisers (e.g. Sphinx-4 [Walker et al.2004] and Google’s web-based ASR [Schalkwyk et al.2010]; see Baumann.etal16 for a comparison). One reason for this is that naturalistic spoken interaction is excessively expensive and time-consuming to transcribe and annotate on a level of granularity fine-grained enough to reflect the strict time-linear nature of these phenomena.
In this paper, we present a new dialogue data set - the BURCHAK corpus - collected using a new incremental variant of the DiET chat-tool [Healey et al.2003, Mills and Healeysubmitted]111Available from https://sites.google.com/site/hwinteractionlab/babble, which enables character-by-character, text-based interaction between pairs of participants, and which circumvents all transcription effort as all this data, including all timing information at the character level is automatically recorded.
The chat-tool is designed to support, elicit, and record at a fine-grained level, dialogues that resemble the face-to-face setting in that turns are: (1) constructed and displayed incrementally as they are typed; (2) transient; (3) potentially overlapping as participants can type at the same time; (4) not editable, i.e. deletion is not permitted - see Sec. 3 and Fig. 2. Thus, we have been able to collect many of the important phenomena mentioned above that arise from the inherently incremental nature of language processing in dialogue - see table 1.
Having presented the data set, we then go on to introduce a generic n-gram framework for building user simulations for either task-oriented or non-task-oriented dialogue systems from this data-set, or others constructed using the same tool. We apply this framework to train a robust user model that is able to simulate the tutor’s behaviour to interactively teach (visual) word meanings to a Reinforcement Learning dialogue agent.
2 Related Work
In this section, we will present an overview of relevant data-sets and techniques for Human-Human dialogue collection, as well as approaches to user simulation based on realistic data.
2.1 Human-Human Data Collection
There are several existing corpora of human-human spontaneous spoken dialogue, such as SWITCHBOARD [Godfrey et al.1992], and the British National Corpus, which consist of open, unrestricted telephone conversations between people, where there are no specific tasks to be achieved. These datasets contain many of the incremental dialogue phenomena that we are interested in, but there is no shared visual scene between participants, meaning we cannot use such data to explore learning of perceptually grounded language. More relevant is the MAPTASK corpus [Thompson et al.1993], where dialogue participants both have maps which are not shared. This dataset allows investigation of negotiation dialogue, where object names can be agreed, and so does support some work on language grounding. However, in the MAPTASK, grounded word meanings are not taught by ostensive definition as is the case in our new dataset.
We further note that the DiET Chat Tool [Healey et al.2003, Mills and Healeysubmitted] while designed to elicit conversational structures which resemble face-to-face dialogue (see examples in table 1), circumvents the need for the very expensive and time-consuming step of spoken dialogue transcription, but nevertheless produces data at a very fine-grained level. It also includes tools for creating more abstract (e.g. turn-based) representations of conversation.
2.2 User Simulation
Training a dialogue strategy is one of the fundamental tasks of the user simulation. Approaches to user simulation can be categorised based on the level of abstraction at which the dialogue is modeled: 1) the intention-level has become the most popular user model that predicts the next possible user dialogue action according to the dialogue history and the user/task goal [Eckert et al.1997, Asri et al.2016, Cuayáhuitl et al.2005, Chandramohan et al.2012, Eshky et al.2012, Ai and Weng2008, Georgila et al.2005]; 2) on the word/utterance-level, instead of dialogue action, the user simulation can also be built for predicting the full user utterances or a sequence of words given specific information [Chung2004, Schatzmann et al.2007b]; and 3) on the semantic-level, the whole dialogue can be modeled as a sequence of user behaviors in the semantic representation [Schatzmann et al.2007a, Schatzmann et al.2007c, Kalatzis et al.2016].
There are also some user simulations built on multiple levels. For instance, jungLKJL:2009:csl integrated different data-driven approaches on intention and word levels to build a novel user simulation. The user intent simulation is for generating user intention patterns, and then a two-phase data-driven domain-specific user utterance simulation is proposed to produce a set of structured utterances with sequences of words given a user intent and select the best one using the BLEU score. The user simulation framework we present below is generic in that one can use it to train user simulations on a word-by-word, utterance-by-utterance, or action-by-action levels, and it can be used for both goal-oriented and non-goal-oriented domains.
3 Data Collection using the DiET Chat Tool and a Novel Shape and Colour Learning Task
In this section, we describe our data collection method and process, including the concept learning task given to the human participants.
The DiET experimental toolkit
This is a custom-built Java application [Healey et al.2003, Mills and Healeysubmitted] that allows two or more participants to communicate in a shared chat window. It supports live, fine-grained and highly local experimental manipulations of ongoing human-human conversation (see e.g. [Eshghi and Healey2015]). The variant we use here supports text-based, character-by-character, interaction between pairs of participants, and here we use it solely for data-collection, where everything that the participants type to each other passes through the DiET server, which transmits the utterance to the other clients on the character level and all are displayed on the same row/track in the chat window (see Fig. (a)a) - this means that when participants type at the same time in interruptions and turn overlaps, their utterances will be all jumbled up (see Fig. 1b). To simulate the transience of speech in face-to-face conversation with its characteristic phenomena, all utterances in the chat window fade out after 1 second. Furthermore, like in speech, deletes are not permitted: if a character is typed, it cannot be deleted. The chat-tool is thus designed to support, elicit, and record at a fine-grained level, dialogues that resemble face-to-face dialogue in that turns are: (1) constructed and displayed incrementally as they are typed; (2) transient; (3) potentially overlapping; (4) not editable, i.e. deletion is not permitted.
Task and materials
The learning/tutoring task given to the participants involves a pair of participants who talk about visual attributes (e.g. colour and shape) through a sequence of 9 visual objects, one at a time. The objects are created based on a 3 x 3 visual attribute matrix (including 3 colours and 3 shapes (see Fig.(b)b)). This task is assumed in a second-language learning scenario, where each visual attribute, instead of standard English words, is assigned to a new unknown word in a made-up language, e.g. “sako” for red and “burchak” for square: participants are not allowed to use any of the usual colour and shape words from the English language. We design the task in this way to collect data for situations where a robot has to learn the meaning of human visual attribute terms. In such a setting the robot has to learn the perceptual groundings of words such as “red”. However, humans already know these groundings, so to collect data about teaching such perceptual meanings, we invented new attribute terms whose groundings the Learner must discover through interaction.
The overall goal of the task is for the learner to identify the shape and colour of the presented objects correctly for as many objects as possible. So the tutor initially needs to teach the learner about these using the presented objects. For this, the tutor is provided with a visual dictionary of the (invented) colour and shape terms (see Fig. 2), but the learner only ever sees the object itself. The learner will thus gradually learn these and be able to identify them, so that initiative in the conversation tends to be reversed on later objects, with the learner making guesses and the tutor either confirming these or correcting them.
Forty participants were recruited from among students and research staff from various disciplines at Heriot-Watt University, including 22 native speakers and 18 non-native speakers.
The participants in each pair were randomly assigned to experimental roles (Tutor vs. Learner). They were given written instructions about the task and had an opportunity to ask questions about the procedure. They were then seated back-to-back in the same room, each at a desk with a PC displaying the appropriate task window and chat client window (see Fig.2). They were asked to go through all visual objects in at most 30 minutes and then the Learner was assessed to check how many new colour and shape words they had learned. Each participant was paid £ for participation. The best performing pair was also given a £20 Amazon Voucher as prize.
4 The BURCHAK Corpus Statistics
Using the above procedure, we have collected 177 dialogues (each about one visual object) with a total of 2454 turns, where a turn is defined222Note that the definition of a ‘turn’ in an incremental system is somewhat arbitrary. as a sequence of consecutive characters typed by a single participant with a delay of no more than 1100 ms between the characters. Figure (a)a shows the distribution of dialogue length (i.e. number of turns) in the corpus. where the average number of turns per dialogue is .
4.2 Incremental Dialogue Phenomena
As noted, the DiET Chattool is designed to elicit and record conversations that resemble face-to-face dialogue. In this paper, we report specifically on a variety of dialogue phenomena that arise from the incremental nature of language processing. These are the following:
Overlapping: where interlocutors speak/type at the same time (i.e. the original corpus contains over 800 overlaps), leading to jumbled up text on the DiET interface (see Fig. 1);
Self-Correction: a kind of correction that is performed incrementally in the same turn by a speaker; this can either be conceptual, or simply repairing a misspelling or mis-pronunciation.
Self-Repetition: the interlocutor repeats words, phrases, even sentences, in the same turn.
Continuation (aka Split-Utterance): the interlocutor continues the previous utterance (by herself or the other) where either the second part, or the first part or both are syntactically incomplete.
Filler: allows the interlocutor to further plan her utterance while keeping the floor. These can also elicit continuations from the other [Howes et al.2012]. This is performed using tokens such as ‘urm’, ‘err’, ‘uhh’, or ‘…’.
For annotating self-corrections, self-repetitions and continuations we have loosely followed protocols from Purver.etal09,Colman.Healey11. Figure (d)d shows how frequently these incremental phenomena occur in the BURCHAK Corpus. This figure excludes Overlaps which were much more frequent: in total, which amounts to about per dialogue.
4.3 Cleaning up the data for the User Simulation
For the purpose of the annotation of Dialogue Actions, subsequent training of the user simulation, and the Reinforcement Learning described below, we cleaned up the original corpus as follows: 1) we fixed the spelling mistakes which were not repaired by the participants themselves; 2) we also removed snippets of conversation where the participants had misunderstood the task (e.g. trying to describe the objects or where they had used other languages) (see Figure 3); as well as 3) removing emoticons (which frequently occurs in the chat tool).
We trained a simulated tutor based on this cleaned up data (see below, Section 5).
4.4 Dialogue Actions and their frequencies
The cleaned up data was annotated for the following dialogue actions:
Inform: the action to inform the correct attribute words of an object to the partner, including statement, question-answering, correction, , e.g. “this is a suzuli burchak” or “this is sako”;
Acknowledgment: the ability to process confirmations from the tutor/the learner, e.g. “Yes, it’s a square”.
Rejection: the ability to process negations from the tutor, e.g. “no, it’s not red”;
Asking: the action to ask WH or polar questions requesting correct information, e.g. “what colour is this?” or “is this a red square?”.
Focus: the action to switch the dialogue topic onto specific objects or attributes, e.g. “let’s move to shape now”;
Clarification: the action to clarify the categories for particular attribute names, e.g. “this is for color not shape”;
Checking: the action to check whether the partner understood, e.g. “get it?”;
Repetition: the action to request Repetitions to double-check the learned knowledge, e.g. “can you repeat the color again?”;
Offer-Help: the action to help the partner answer questions, occurs frequently when the learner cannot answer it immediately, e.g. “L: it is a … T: need help? L: yes. T: a sako burchak.”;
Fig. (c)c shows how often each dialogue action occurs in the data set; and Fig. (b)b shows the frequencies of these actions by the learner and the tutor individually in each dialogue turn. In contrast with a lot of previous work which assumes a single action per turn, here we get multiple actions per turn (see Table 1) In terms of the Learner behavior, the learner mostly performs a single action per turn. On the other hand, although the majority of the dialogue turns on the tutor side also have a single action, about of the dialogue turns perform more than one action.
5 TeachBot User Simulation
Here we describe the generic user simulation framework, based on n-grams, for building user simulation from this type of incremental corpus. We apply this framework to train a TeachBot user simulator that is used to train a RL interactive concept learning agent, both here, and in future work. The model is here trained from the cleaned up version of the corpus.
5.1 The N-gram User Simulation
The proposed user model is a compound n-gram simulation that the probability () of an item (an action or utterance from the tutor in our work) is predicted based on a sequence of the most recent words () from the previous utterance and additional dialogue context parameters :
where represent additional conditions for specific user/task goals (e.g. goal completion as well as previous dialogue context).
For this specific task, the additional dialogue conditions () are as follows: (1) the color state () for whether the color attribute is identified correctly, (2) the shape state () for whether the shape attribute is identified correctly, as well as 3) the previous context () for which attribute (colour or shape) is currently under discussion.
In order to reduce mismatch risk, the simulation model is able to back-off to smaller n-grams when it cannot find any n-grams matched to the current word sequence and conditions. To eliminate the search restriction by the additional conditions, we applied the nearest neighbors algorithm to search for the n-gram matches by calculating the Hamming distance of each pair of n-grams.
The n-gram user simulation is generic, as it is designed to handle the item prediction on multiple levels, on which the predicted item, , can be assigned either to (1) a full user utterance () on the utterance level; (2) a combined sequence of dialogue actions (); or alternatively (3) the next word/lexical token. During the simulation, the n-gram model chooses the next item according to the distribution of n-grams. In terms of the action level, a user utterance will be chosen upon a distribution of utterance templates collected from the corpus and combined given dialogue actions . The tutor simulation we train here is at the level of the action and utterance, and is evaluated on the same levels below. However, the framework can be used to train to predict fully incrementally on a word-by-word basis. In this case, the in Eq.1 will contain not only a sequence of words from the previous system utterance, but also words from the current speaker (the tutor itself as it is generating).
5.2 Evaluation of the User Simulation
We evaluate the proposed user simulation based on the turn-level evaluation metrics by[Keizer et al.2012], in which evaluation is done on a turn-by-turn basis. Evaluation is done based on the cleaned up corpus (see Section 4). We investigate the performance of the user model on two levels: the utterance level and the action level.
The evaluation is done by comparing the distribution of the predicted actions or utterances with the actual distributions in the data. We report two measures: the Accuracy and Kullback-Leibler Divergence (cross-entropy) to quantify how closely the simulated user responses resemble the real user responses in the BURCHAK corpus. Accuracy () measures the proportion of times an utterance or dialogue act sequence () is predicted correctly by the simulator, given a particular set of conditions (). To calculate this, all existing combinations in the data of the values of these variables are tried. If the predicted action or utterance occurs in the data for these given conditions, we count the prediction as correct.
Kullback-Leibler Divergence (KLD) () is applied to compare the predicted distributions and the actual one in the corpus (see Eq.2).
Table 2 shows the results: the user simulation on both utterance and action levels achieves good performance. The action-based user model, on a more abstract level, would likely be better as it is less sparse, and produces more variation in the resulting utterances.
Ongoing work involves using BURCHAK to train a word-by-word incremental tutor simulation, capable of generating all the incremental phenomena identified earlier.
6 Training a prototype concept learning agent from the BURCHAK corpus
In order to demonstrate how the BURCHAK corpus can be used, we train and evaluate a prototype interactive learning agent using Reinforcement Learning (RL) on the collected data. We follow previous task and experiment settings (see [Yu et al.2016b, Yu et al.2016c]) to compare the learned RL-based agent with a rule-based agent with the best performance from previous work. Instead of using hand-crafted dialogue examples as before, here we train the RL agent in interaction with the user simulation, itself trained from the BURCHAK data as above.
6.1 Experiment Setup
To compare the performance of the rule-based system and the trained RL-based system in the interactive learning process, we follow all experiment setup, including visual data-set and cross-validation method. We also follow the evaluation metrics provided by yu-eshghi-lemon:2016:sigdial : Overall Performance Ratio () to measures the trade-offs between the cost to the tutor and the accuracy of the learned meanings, i.e. the classifiers that ground our colour and shape concepts. (see Eq.3).
i.e. the increase in accuracy per unit of the cost, or equivalently the gradient of the curve in Fig. 5 We seek dialogue strategies that maximise this.
The cost measure reflects the effort needed by a human tutor in interacting with the system. Skocaj et. al. Skocaj2009a point out that a comprehensive teachable system should learn as autonomously as possible, rather than involving the human tutor too frequently. There are several possible costs that the tutor might incur: refers to the cost (assigned to points) of the tutor providing information on a single attribute concept (e.g. “this is red” or “this is a square”); is the cost ( points) for a simple confirmation (like “yes”, “right”) or rejection (such as “no”); is the cost of correction ( points) for a single concept (e.g. “no, it is blue” or “no, it is a circle”).
6.2 Results & Discussion
Fig. 5 plots Accuracy against Tutoring Cost directly. The gradient of this curve corresponds to increase in Accuracy per unit of the Tutoring Cost: a measure of the trade-off between accuracy of learned meanings and tutoring cost.
The result shows that the RL-based learning agent achieves a comparable performance with the rule-based system.
Table 3 shows an example dialogue between the learned concept learning agent and the tutor simulation, where the user model simulates the tutor behaviour (T) for the learning tasks. In this example, the utterance produced by the simulation involves two incremental phenomena, i.e. a self-correction and a continuation, though note that these have not been produced on a word-by-word level.
We presented a new data collection tool, a new data set, and and associated dialogue simulation framework which focuses on visual language grounding and natural, incremental dialogue phenomena. The tools and data are freely available and easy to use.
We have collected new human-human dialogue data on visual attribute learning tasks, which are then used to create a generic n-gram user simulation for future research and development. We used this n-gram user model to train and evaluate an optimized dialogue policy, which learns grounded word meanings from a human tutor, incrementally, over time. This dialogue policy optimisation learns a complete dialogue control policy from the data, in contrast to earlier work [Yu et al.2016c] which only optimised confidence thresholds, and where dialogue control was entirely rule-based.
Ongoing work further uses the data and simulation framework here to train a word-by-word incremental tutor simulation, with which to learn complete, incremental dialogue policies, i.e. policies that choose system output at the lexical level [Eshghi and Lemon2014]. To deal with uncertainty this system in addition takes all the visual classifiers’ confidence levels directly as features in a continuous space MDP.
This research is supported by the EPSRC, under grant number EP/M01553X/1 (BABBLE project333https://sites.google.com/site/hwinteractionlab/babble), and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 688147 (MuMMER project444http://mummer-project.eu/).
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