MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
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Abstract (arXiv)
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.
Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 26 Sep 2021.
Builds on
26 of 35 references.
References · 26
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- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
- 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 691
- 2018CoQA: A Conversational Question Answering Challengecited by 674
- 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
- 2018QuAC : Question Answering in Contextcited by 379
- 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
- 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
14 more
- 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
- 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 160
- 2020Open-Retrieval Conversational Question Answeringcited by 99
- 2020doc2dial: A Goal-Oriented Document-Grounded Dialogue Datasetcited by 88
- 2018Interpretation of Natural Language Rules in Conversational Machine Readingcited by 72
- 2020Relevance-guided Supervision for OpenQA with ColBERTcited by 71
- 2021Few-Shot Conversational Dense Retrievalcited by 71
- 2020DoQA -- Accessing Domain-Specific FAQs via Conversational QAcited by 42
- 2020Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Readingcited by 33
- 2021Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Taskscited by 32
- 2021Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoringcited by 29
- 2020Topic Propagation in Conversational Searchcited by 10
- 2021A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answeringcited by 7
- 2021Weakly-Supervised Open-Retrieval Conversational Question Answeringcited by 4
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Citing papers per year: 2022: 3; 2023: 18; 2024: 15; 2025: 7; 2026: 10.
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