Tianyu Zhao


Multi-Referenced Training for Dialogue Response Generation
Tianyu Zhao | Tatsuya Kawahara
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this work, we first analyze the training objective of dialogue models from the view of Kullback-Leibler divergence (KLD) and show that the gap between the real world probability distribution and the single-referenced data’s probability distribution prevents the model from learning the one-to-many relations efficiently. Then we explore approaches to multi-referenced training in two aspects. Data-wise, we generate diverse pseudo references from a powerful pretrained model to build multi-referenced data that provides a better approximation of the real-world distribution. Model-wise, we propose to equip variational models with an expressive prior, named linear Gaussian model (LGM). Experimental results of automated evaluation and human evaluation show that the methods yield significant improvements over baselines.


Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System
Shuying Zhang | Tianyu Zhao | Tatsuya Kawahara
Proceedings of the 28th International Conference on Computational Linguistics

Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic constraint. Under the hypothesis that a response and its context have higher relevance when they share the same topics, the topical constraint encourages the topics of a response to match its context by conditioning response decoding on topic words’ embeddings. The semantic constraint, which encourages a response to be semantically related to its context by regularizing the decoding objective function with semantic distance, is proposed. Optimal transport is applied to compute a weighted semantic distance between the representation of a response and the context. Generated responses are evaluated by automatic metrics, as well as human judgment, showing that the proposed method can generate more topic-relevant and content-rich responses than conventional models.

Designing Precise and Robust Dialogue Response Evaluators
Tianyu Zhao | Divesh Lala | Tatsuya Kawahara
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust. In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models. Experimental results demonstrate that the proposed evaluator achieves a strong correlation (> 0.6) with human judgement and generalizes robustly to diverse responses and corpora. We open-source the code and data in https://github.com/ZHAOTING/dialog-processing.


A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System
Tianyu Zhao | Tatsuya Kawahara
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

In spoken dialog systems (SDSs), dialog act (DA) segmentation and recognition provide essential information for response generation. A majority of previous works assumed ground-truth segmentation of DA units, which is not available from automatic speech recognition (ASR) in SDS. We propose a unified architecture based on neural networks, which consists of a sequence tagger for segmentation and a classifier for recognition. The DA recognition model is based on hierarchical neural networks to incorporate the context of preceding sentences. We investigate sharing some layers of the two components so that they can be trained jointly and learn generalized features from both tasks. An evaluation on the Switchboard Dialog Act (SwDA) corpus shows that the jointly-trained models outperform independently-trained models, single-step models, and other reported results in DA segmentation, recognition, and joint tasks.


Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks
Tianyu Zhao | Tatsuya Kawahara
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Dialog act segmentation and recognition are basic natural language understanding tasks in spoken dialog systems. This paper investigates a unified architecture for these two tasks, which aims to improve the model’s performance on both of the tasks. Compared with past joint models, the proposed architecture can (1) incorporate contextual information in dialog act recognition, and (2) integrate models for tasks of different levels as a whole, i.e. dialog act segmentation on the word level and dialog act recognition on the segment level. Experimental results show that the joint training system outperforms the simple cascading system and the joint coding system on both dialog act segmentation and recognition tasks.


Talking with ERICA, an autonomous android
Koji Inoue | Pierrick Milhorat | Divesh Lala | Tianyu Zhao | Tatsuya Kawahara
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue