Dingnan Jin


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Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
Hongru Liang | Jia Liu | Weihong Du | Dingnan Jin | Wenqiang Lei | Zujie Wen | Jiancheng Lv
Findings of the Association for Computational Linguistics: ACL 2023

The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However, current methods have trouble answering complex questions. Therefore, we introduce the knowing-how & knowing-that task that requires the model to answer factoid-style, procedure-style, and inconsistent questions about user manuals. We resolve this task by jointly representing the sTeps and fActs in a gRAh (TARA), which supports a unified inference of various questions. Towards a systematical benchmarking study, we design a heuristic method to automatically parse user manuals into TARAs and build an annotated dataset to test the model’s ability in answering real-world questions. Empirical results demonstrate that representing user manuals as TARAs is a desired solution for the MRC of user manuals. An in-depth investigation of TARA further sheds light on the issues and broader impacts of future representations of user manuals. We hope our work can move the MRC of user manuals to a more complex and realistic stage.

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TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning
Yue Chen | Dingnan Jin | Chen Huang | Jia Liu | Wenqiang Lei
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Efficiently retrieving FAQ questions that match users’ intent is essential for online customer service. Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions. However, the conversation context contains noise, e.g., users may click questions they don’t like, leading to inaccurate semantics modeling. To tackle this, we introduce tags of FAQ questions, which can help us eliminate irrelevant information. We later integrate them into a reinforcement learning framework and minimize the negative impact of irrelevant information in the dynamic conversation context. We experimentally demonstrate our efficiency and effectiveness on conversational FAQ retrieval compared to other baselines.