CSS : Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022)

Abstract: Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from external labeled dialogue data (e.g., from question answering, dialogue summarization, machine reading comprehension tasks, etc.) into DST, whereas collecting a large amount of external labeled data is laborious, and the external data may not effectively contribute to the DST-specific task. In this paper, we propose a few-shot DST framework called CSS, which Combines Self-training and Self-supervised learning methods. The unlabeled data of the DST task is incorporated into the self-training iterations, where the pseudo labels are predicted by a DST model trained on limited labeled data in advance. Besides, a contrastive self-supervised method is used to learn better representations, where the data is augmented by the dropout operation to train the model. Experimental results on the MultiWOZ dataset show that our proposed CSS achieves competitive performance in several few-shot scenarios.

## introduction dialogue state tracking (dst) is an essential subtask in a task-oriented dialogue system @xcite @xcite . it predicts the dialogue state corresponding to the user's intents at each dialogue turn, which will be used to extract the preference and generate the natural language response @xcite @xcite @xcite @xcite @xcite . figure 1 gives an example of dst in a conversation, where the dialogue state is accumulated and updated after each turn. usr: hi i am looking for a restaurant in the north that serves asian oriental food. sys: i would recommend saigon city. would you like to make a reservation? usr: that sounds great! we would like a reservation for monday at 16:45 for 6 people. can i get the reference number for our reservation? training a dst model requires plenty of dialogue corpus containing dialogue utterances and human-annotated state labels, whereas annotating is costly. therefore, the dst models are expected to have acceptable performance when trained with limited labeled data, i.e., in the few-shot cases @xcite . previous studies on few-shot dst solve the data scarcity issue mainly by leveraging external labeled dialogue corpus to pre-train the language models, which are then transferred into the dst task @xcite @xcite . however, there exist several disadvantages: first, collecting a large amount of external labeled data is still laborious; second, utilizing the external data is heavily dependent on computational resources since the language models have to be further pre-trained; third, the external data always comes from different conversation scenarios and nlp tasks, such as dialogues in multi topics, question answering, dialogue summary, etc. the data types and distributions differ from the dst-specific training data, making it less efficient to transfer the learned knowledge into dst. we consider utilizing the unlabeled data of the dst task, which is easy to access and has similar contents to the limited labeled data, so that the dst model can be enhanced by training on an enlarged amount of data corpus. in this pa- initialization teacher model pseudo label prediction student model iteration l u l+u model update training process non-training process 𝑆 𝑗 context input 𝑉 𝑗 𝑖 bert context (ℎ𝑚, ℎ 𝑘 -) push apart (ℎ𝑚, ℎ𝑚 + ) pull close mini-batch data augmentation slot-context attention slot-value matching (a) overall framework (b) model architecture bert state (slot) bert state (value) ℎ 𝑆𝑗 ℎ 𝑉 𝑗 𝑖 𝑤 𝑆𝑗 𝑡 finetune fixed fixed is the model architecture for both teacher and student, where the red dashed box is the illustration of the self-supervised learning object through the dropout augmentation: narrow the distance between each instance and its corresponding augmented one (pull close), enlarge its distance to the rest in the same batch in representation area (push apart). ## related work few-shot dst focuses on the model performance with limited labeled training data, which overcomes the general data scarcity issue. existing dst models enhance the few-shot performance mainly by incorporating external data of different tasks to further pre-train a language model, which is still collection and computational resources demanding @xcite @xcite . inspired by self-training that incorporates predicted pseudo labels of the unlabeled data to enlarge the training corpus @xcite @xcite , in this paper, we build our framework upon the noisystudent method @xcite to enhance the dst model in few-shot cases. self-supervised learning trains a model on an auxiliary task with the automatically obtained ground-truth @xcite @xcite @xcite . as one of the self-supervised approaches, contrastive learning succeeds in various nlp-related tasks, which helps the model learn high-quality representations @xcite @xcite . in this paper, we construct contrastive data pairs by the dropout operation to train the dst model, which does not need extra supervision. ## methology figure 1 shows the css framework, where (a) is the overall training framework, and (b) is the architecture of both teacher and student models. our css follows the noisystudent self-training framework @xcite . after deriving a teacher dst model trained with labeled data, it's continuously trained and updated into the student dst model with both labeled and unlabeled data, where the pseudo labels of the unlabeled data are synchronously predicted. unlike the original noisystudent augmenting training data only in the student training stage, we implement the contrastive selfsupervised learning method in both training teacher and student models, where each training instance is augmented through a dropout operation, and the model is trained to group each instance with its augmented pair closer and diverse it far from the rest in the same batch. ## experiments in this section, we first give the experimental dataset and training details, then show the experimental results compared with several existing baselines, ablation studies in both multi-domain and single-domain accuracy, and the error analysis.