# Open-Retrieval Conversational Machine Reading

arXiv: https://arxiv.org/abs/2102.08633 · HTML: https://mlsanity.com/paper/2102.08633/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2005.11364/) (2020)
- [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://mlsanity.com/paper/1908.08167/) (2019)
- [Interpretation of Natural Language Rules in Conversational Machine Reading](https://mlsanity.com/paper/1809.01494/) (2018)
- [Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading](https://mlsanity.com/paper/2010.01838/) (2020)
- [Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling](https://mlsanity.com/paper/1906.06893/) (2019)
- [Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction](https://mlsanity.com/paper/2011.13137/) (2020)
- [Improving Question Generation With to the Point Context](https://mlsanity.com/paper/1910.06036/) (2019)
- [E3: Entailment-driven Extracting and Editing for Conversational Machine Reading](https://mlsanity.com/paper/1906.05373/) (2019)
- [Attending to Future Tokens For Bidirectional Sequence Generation](https://mlsanity.com/paper/1908.05915/) (2019)
- [Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading](https://mlsanity.com/paper/2005.12484/) (2020)
- [Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge](https://mlsanity.com/paper/2102.05474/) (2021)
- [Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization](https://mlsanity.com/paper/1911.06948/) (2019)
- [Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning](https://mlsanity.com/paper/2010.01495/) (2020)
- [Multi-Granular Text Encoding for Self-Explaining Categorization](https://mlsanity.com/paper/1907.08532/) (2019)
- [SRQA: Synthetic Reader for Factoid Question Answering](https://mlsanity.com/paper/2009.01630/) (2020)

## Built on by (most cited first, 11 of 11)

- [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://mlsanity.com/paper/2205.04040/) (2022)
- [When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification](https://mlsanity.com/paper/2602.11199/) (2026)
- [Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation](https://mlsanity.com/paper/2109.02046/) (2021)
- [LogiGAN: Learning Logical Reasoning via Adversarial Pre-training](https://mlsanity.com/paper/2205.08794/) (2022)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)
- [ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2209.11484/) (2022)
- [Towards End-to-End Open Conversational Machine Reading](https://mlsanity.com/paper/2210.07113/) (2022)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Unsupervised Technique To Conversational Machine Reading](https://mlsanity.com/paper/2106.15247/) (2021)
- [Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2212.09353/) (2022)
- [How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context](https://mlsanity.com/paper/2308.13760/) (2023)

## Abstract (arXiv)

In conversational machine reading, systems need to interpret natural language rules, answer high-level questions such as "May I qualify for VA health care benefits?", and ask follow-up clarification questions whose answer is necessary to answer the original question. However, existing works assume the rule text is provided for each user question, which neglects the essential retrieval step in real scenarios. In this work, we propose and investigate an open-retrieval setting of conversational machine reading. In the open-retrieval setting, the relevant rule texts are unknown so that a system needs to retrieve question-relevant evidence from a collection of rule texts, and answer users' high-level questions according to multiple retrieved rule texts in a conversational manner. We propose MUDERN, a Multi-passage Discourse-aware Entailment Reasoning Network which extracts conditions in the rule texts through discourse segmentation, conducts multi-passage entailment reasoning to answer user questions directly, or asks clarification follow-up questions to inquiry more information. On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin. In addition, we conduct in-depth analyses to provide new insights into this new setting and our model.