Haifeng Tang


2022

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Post-Training Dialogue Summarization using Pseudo-Paraphrasing
Qi Jia | Yizhu Liu | Haifeng Tang | Kenny Zhu
Findings of the Association for Computational Linguistics: NAACL 2022

Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models. These features either require additional knowledge to recognize or make the resulting models harder to tune. To bridge the format gap between dialogues and narrative summaries in dialogue summarization tasks, we propose to post-train pretrained language models (PLMs) to rephrase from dialogue to narratives. After that, the model is fine-tuned for dialogue summarization as usual. Comprehensive experiments show that our approach significantly improves vanilla PLMs on dialogue summarization and outperforms other SOTA models by the summary quality and implementation costs.

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ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments
Ruolan Yang | Zitong Li | Haifeng Tang | Kenny Zhu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Existing automatic evaluation systems of chatbots mostly rely on static chat scripts as ground truth, which is hard to obtain, and requires access to the models of the bots as a form of “white-box testing”. Interactive evaluation mitigates this problem but requires human involvement. In our work, we propose an interactive chatbot evaluation framework in which chatbots compete with each other like in a sports tournament, using flexible scoring metrics. This framework can efficiently rank chatbots independently from their model architectures and the domains for which they are trained.

2020

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Multi-turn Response Selection using Dialogue Dependency Relations
Qi Jia | Yizhu Liu | Siyu Ren | Kenny Zhu | Haifeng Tang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multi-turn response selection is a task designed for developing dialogue agents. The performance on this task has a remarkable improvement with pre-trained language models. However, these models simply concatenate the turns in dialogue history as the input and largely ignore the dependencies between the turns. In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Each thread can be regarded as a self-contained sub-dialogue. We also propose Thread-Encoder model to encode threads and candidates into compact representations by pre-trained Transformers and finally get the matching score through an attention layer. The experiments show that dependency relations are helpful for dialogue context understanding, and our model outperforms the state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results on UbuntuV2.