Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning (2020)

Abstract: In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST. Our main motivation is to further bridge the chasm between the need for high accuracy dialogue state tracker and the common reality that only scarce annotated data is available for most real-life dialogue tasks. Specifically, MEDST has two core steps: meta-training with adequate unlabelled data in an automatic way and meta-testing with a few annotated data by supervised learning. In particular, we enhance SEDST via entropy regularization, and investigate semi-supervised learning frameworks based on model-agnostic meta-learning (MAML) that are able to reduce the amount of required intermediate state labelling. We find that by leveraging un-annotated data in meta-way instead, the amount of dialogue state annotations can be reduced below 10% while maintaining equivalent system performance. Experimental results show MEDST outperforms SEDST substantially by 18.7% joint goal accuracy and 14.3% entity match rate on the KVRET corpus with 2% labelled data in semi-supervision.

## introduction task-oriented dialogue systems @xcite are designed to help users to achieve specific goals such as restaurant reservation or navigation inquiry. in recent years, fully neural end-to-end architectures usually take the sequence-to-sequence (se-q2seq) @xcite model to generate dialogue responses from the user inputs and context history @xcite @xcite @xcite . neural generative models for task-oriented dialogue systems have achieved promising performance on generation tasks if given a huge training dataset and detailed annotations @xcite r , , various approaches @xcite @xcite @xcite have been proposed recently to conquer this challenge, semi-supervised explicit dialogue state tracker (sedst) @xcite foot_0 is one of them. sedst tries to solve the label-lacking problem by classical semi-supervision. however, the gain with sedst is not as satisfactory when the annotated data is very scarce, only a small fraction of what is expected. in this paper, we focus on improving dialogue performance on top of sedst. our first contribution is to enhance sedst with meta-learning. we propose a maml-based @xcite semisupervision architecture for low-resource. our experiments with the kvret dataset show this integration of maml and sedst can reach comparable dialogue state tracking accuracy with below 10% intermediate annotation. second, we improve ## proposed approaches in this section, we describe the details of medst by starting with brief overviews of sedst. ## main results in the main experiments, we take the original sedst faithful to @xcite as baseline. figure 3 plots another evaluation metric bleu for medst in different amounts of labelled data. medst with only 10% labelled data can reach the similar bleu as sedst, which requires 25%. in new domain adaption experiments, our model medst performs meta-training on unlabelled data from source domains and meta-testing on 5% labelled data from target domain. sedst inputs source domain's unlabelled data and 5% target domain's labelled data together to perform one-stage training process. from the results shown in ## conclusions in this work, we investigate maml algorithm and entropy regularization on top of sedst for lowresource dialogue tasks. we demonstrate the superiority of our proposed model medst with lowresource labelled data and perform a fair amount of ablation studies. medst can also be adapted to new domains with much better performance. future work includes exploring more internal mechanism with the combination of semi-supervision and maml for other tasks.