Bin Zhou


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Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing
Qian Liu | Dejian Yang | Jiahui Zhang | Jiaqi Guo | Bin Zhou | Jian-Guang Lou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Incomplete Utterance Rewriting as Semantic Segmentation
Qian Liu | Bei Chen | Jian-Guang Lou | Bin Zhou | Dongmei Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.

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You Impress Me: Dialogue Generation via Mutual Persona Perception
Qian Liu | Yihong Chen | Bei Chen | Jian-Guang Lou | Zixuan Chen | Bin Zhou | Dongmei Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose Pˆ2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, Pˆ2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.


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A Split-and-Recombine Approach for Follow-up Query Analysis
Qian Liu | Bei Chen | Haoyan Liu | Jian-Guang Lou | Lei Fang | Bin Zhou | Dongmei Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.