Pengwei Hu


pdf bib
Diversifying Dialogue Generation with Non-Conversational Text
Hui Su | Xiaoyu Shen | Sanqiang Zhao | Zhou Xiao | Pengwei Hu | Randy Zhong | Cheng Niu | Jie Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging non-conversational text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.


pdf bib
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
Hui Su | Xiaoyu Shen | Rongzhi Zhang | Fei Sun | Pengwei Hu | Cheng Niu | Jie Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent research has achieved impressive results in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challenge is the frequently occurred coreference and information omission in our daily conversation, making it hard for machines to understand the real intention. In this paper, we propose rewriting the human utterance as a pre-process to help multi-turn dialgoue modelling. Each utterance is first rewritten to recover all coreferred and omitted information. The next processing steps are then performed based on the rewritten utterance. To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network. We show the proposed architecture achieves remarkably good performance on the utterance rewriting task. The trained utterance rewriter can be easily integrated into online chatbots and brings general improvement over different domains.