Affect refers to emotion, sentiment, mood, and attitudes including subjective evaluations, opinions, and speculations . Psychological models of affect have been utilized by other extensive computational research to operationalize and measure users’ opinions, intentions, and expressions. Understanding affective responses with in conversations is an important first step for studying affect and has attracted a growing amount of research attention recently [20, 4, 19]. The affective understanding of conversations focuses on the problem of how speakers use emotions to react to a situation and to each other, which can help better understand human behaviors and build better human-computer-interaction systems.
However, modeling affective responses within conversations is relatively challenging since it is hard to quantify the affectiveness  and there are no large-scale labeled dataset about affective levels in responses. In order to facilitate research in modeling interactive affective responses,  introduced a conversation dataset, OffMyChest, building from Reddit, and proposed two tasks: (1) Semi-supervised learning task: predict labels for Disclosure and Supportiveness in sentences based on a small amount of labeled and large unlabeled training data; (2) Unsupervised task: design new characterizations and insights to model conversation dynamics. The current work focused on the first task.
With limited labeled data and large amount of unlabeled data being given, to alleviate the dependence on labeled data, we combine recent advances in language modeling, semi-supervised learning on text and data augmentations on text to form Semi-Supervised Models via Data Augmentation (SMDA). SMDA consists of two parts: supervised learning over labeled data (Section 4.1
) and unsupervised learning over unlabeled data (Section4.2). Both parts utilize data augmentations to enhance the learning procedures. Our contributions in this work can be summarized into three parts: analysed the OffMyChest dataset in Section 3, proposed a semi-supervised text classification system to classify interactive affective responses classification in Section 4 and described the experimental details and results in Section 5.
2 Related Work
: With transformer-based pre-trained models becoming more and more widely-used, pre-training and fine-tuning framework  with large pre-trained language models are applied into a lot of NLP applications and achieved state-of-the-art performances . Language models [15, 7, 21] or masked language models [3, 10] are pre-trained over a large amount of text from Wikipedia and then fine-tuned on specific tasks like text classifications. Here we built our SMDA system based on such framework.
Data Augmentation on Text
: When the amount of labeled data is limited, one common technique for handling the shortage of data is to augment given data and generate more training “augmented” data. Previous work has utilized simple operations like synonym replacement, random insertion, random swap and random deletion for text data augmentation . Another line of research applied neural models for augmenting text by generating paraphrases via back translations  and monotone submodular function maximization . Building on those prior work, we utilized back translations as our augment methods on both labeled and unlabeled sentences.
Semi-Supervised Learning on Text Classification
: One alternative to deal with the lack of labeled data is to utilize unlabeled data in the learning process, which is denoted as Semi-Supervised Learning (SSL), since unlabeled data is usually easier to get compared to labeled data. Researchers has made use of variational auto encoders (VAEs) [2, 22, 6], self-training [11, 5, 12], consistency regularization [14, 13, 18]
to introduce extra loss functions over unlabeled data to help the learning of labeled samples. VAEs utilize latent variables to reconstruct input labeled and unlabeled sentences and predict sentence labels with these latent variables; self-training adds unlabeled data with high-confidence predictions as pseudo labeled data during training process and consistency regularization forces model to output consistent predictions after adding adversarial noise or performing data augmentations to input data. We combined self-training, entropy minimization and consistency regularization in our system for unlabeled sentences.
3 Data Analysis and Pre-processing
Researching how human initiate and hold conversations has attracted increasing attention those days, as it can help us better understand how human behave over conversations and build better AI systems like social chatbot to communicate with people. In this section, we took a closer look at the conversation dataset, OffMyChest , for better understanding and modeling interactive affective responses. Specifically, we describe certain characteristics of this dataset and our pre-processing steps.
3.1 Label Definition
For each comment of a post on Reddit,  annotated them with 6 labels: Information_disclosure representing some degree of personal information in comments; Emotional_disclosure representing comments containing certain positive or negative emotions; Support referring to comments offering social support like advice; General_support representing that comments are offering general support through quotes and catch phrases, with Information_support offering specific information like practical advice, and Emotional_support offering sympathy, caring or encouragement. Each comment can belong to multiple categories.
|Labeled Train Set||Dev Set||Test Set||Unlabeled Train Set|
3.2 Data Statics
In OffMyChest corpus, there are 12,860 labeled sentences and over 420k unlabeled sentences for training, 5,000 unlabeled sentences for test. The label distributions of labeled sentences are showed in Fig. 1. To train and evaluate our systems, we randomly split the given labeled sentence set into train, development and test set. The data statics are shown in Table 1. We tuned hyper-parameters and chose best models based on performance on dev set, and reported model’s performance on test set.
We utilized XLNet-cased-based tokenizer111https://huggingface.co/transformers/model_doc/xlnet.html#xlnettokenizer to split each sentence into tokens. We showed the cumulative sentence length distribution in Fig. 2, 95% comments have less than 64 tokens. Thus we set the maximum sentence length to 64, and remained the first 64 tokens for sentences that exceed the limit. As for data augmentations, we made use of back translation with German as middle language to generate paraphrases for given sentences. Specifically, we loaded translation model from Fairseq222https://github.com/pytorch/fairseq, translated given sentences from English to German, and then translated them back to English. Also to increase the diversity of generated paraphrases, we employed random sampling with a tunable temperature (0.8) instead of beam search for the generation. We describe some examples in Table 2.
We convert this 6-class affective response classification task into 6 binary classification tasks, namely whether each sentence belongs to each category or not (labeled with 1 or 0). For each binary classification task, given a set of labeled sentences consisting of samples with labels , where , and a set of unlabeled sentences , our goal is to learn the classifier . Our SMDA model contains several components: Supervised Learning (Section 4.1) for labeled sentences, Unsupervised Learning (Section 4.2) for unlabeled sentences, and Semi-Supervised Objective Function (Section 4.3) to combine labeled and unlabeled sentences.
4.1 Supervised Learning
4.1.1 Generating Balanced Labeled Training Set
As shown in Fig. 1, the distribution is very unbalanced with respect to General_support, Info_support and Emo_support. In order to get more training sentences with these three types of support and make these three binary classification sub-tasks learn-able with a more balanced training set, we performed data augmentations over sentences with these three labels. Specifically, we paraphrased each sentence by 4 times via back translations and regarded that the augmented sentences have the same labels as original sentences. The comparison distributions are shown in Fig. 3
4.1.2 Supervised Learning for Labeled Sentences
4.2 Unsupervised Learning
4.2.1 Paraphrasing Unlabeled Sentences
We first performed back translations once for each unlabeled sentence to generate the augmented sentence set in the same manner we described before.
4.2.2 Guessing Labels for Unlabeled Sentences
For an unlabeled sentence , we utilized and in Section 4.1 to predict the class distribution:
To avoid the prediction being so close to uniform distribution, we generate low-entropy guessing labelsby a sharpening function :
-norm of the vector. When, the guessed label becomes an one-hot vector.
4.2.3 Self-training for Original Sentences
Inspired by self-training where model is also trained over unlabeled data with high-confidence predictions as their labels, in SMDA, with our guessed labels with respect to original unlabeled sentence , we added such pair into training by minimize the KL Divergence between them:
4.2.4 Entropy Minimization for Original Sentences
One common assumption in many semi-supervised learning methods is that a classifier’s decision boundary should not pass through high-density regions of the marginal data distribution . Thus for original unlabeled sentence , we added another loss term to minimize the entropy of model’s output:
4.2.5 Consistency Regularization for Augmented Sentences
With the assumption that the model should predict similar distributions with input sentences before and after augmentations, we minimized the KL Divergence between outputs with original sentence as input and augmented sentence as input:
Combining all the loss terms for unlabeled sentences, we defined our unsupervised loss terms as:
4.3 Semi-Supervised Objective Function
We combined the supervised and unsupervised learning described above to form our overall semi-supervised objective function:
where is the balanced weight between supervised and unsupervised loss term.
5.1 Model Setup
In SMDA 333The codes and data split will be released later., we only used single model for each task without jointly training and parameter sharing. That is, we trained six separate classifiers on these tasks. Inspired by recent success in pre-trained language models, we utilized the pre-trained weights of XLNet and followed the same fine-tuning procedure as XLNet. We set the initial learning rate for XLNet encoder as 1e-5 and other linear layers as 1e-3. The batch size was selected in
. The maximum number of epochs is set as 20. Hyper-parameters were selected using the performance on development set. The sharpen temperaturewas selected in depending on different tasks. The balanced weight between supervised learning loss and unsupervised learning loss term started from a small number and grew through training process to 1.
Our experimental results are shown in Table 3. We compared our proposed SMDA with BERT and XLNet in terms of accuracy(%) and Macro F1 score. BERT and XLNet achieved similar performance since they both obey the pre-training and fine-tuning manner. When combining with augmented and more balanced labeled data, massive unlabeled data, our SMDA achieved best performance across six binary-classification tasks. And we submitted the classification results on given unlabeled test set.
In this work, we focused on identifying disclosure and supportiveness in conversation responses based on a small labeled and large unlabeled training data via our proposed semi-supervised text classification system : Semi-Supervised Models via Data Augmentation (SMDA). SMDA utilized supervised learning over labeled data and conducted self-training, entropy minimization and consistency regularization over unlabeled data. Experimental results demonstrated that our system outperformed baseline models significantly.
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