MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents

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Abstract

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such a task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based context in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.

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Feng, S., Patel, S. S., Wan, H., & Joshi, S. (2021). MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents. In EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 6162–6176). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.emnlp-main.498

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