# Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

arXiv: https://arxiv.org/abs/2010.01838 · HTML: https://mlsanity.com/paper/2010.01838/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 30 of 33)

- [MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents](https://mlsanity.com/paper/2109.12595/) (2021)
- [DAGN: Discourse-Aware Graph Network for Logical Reasoning](https://mlsanity.com/paper/2103.14349/) (2021)
- [DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing](https://mlsanity.com/paper/2110.04518/) (2021)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://mlsanity.com/paper/2205.04040/) (2022)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [AEON: A Method for Automatic Evaluation of NLP Test Cases](https://mlsanity.com/paper/2205.06439/) (2022)
- [RST Parsing from Scratch](https://mlsanity.com/paper/2105.10861/) (2021)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Open-Retrieval Conversational Machine Reading](https://mlsanity.com/paper/2102.08633/) (2021)
- [LogiGAN: Learning Logical Reasoning via Adversarial Pre-training](https://mlsanity.com/paper/2205.08794/) (2022)
- [Iterative Hierarchical Attention for Answering Complex Questions over Long Documents](https://mlsanity.com/paper/2106.00200/) (2021)
- [COSMic: A Coherence-Aware Generation Metric for Image Descriptions](https://mlsanity.com/paper/2109.05281/) (2021)
- [Exploring Self-supervised Logic-enhanced Training for Large Language Models](https://mlsanity.com/paper/2305.13718/) (2023)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)
- [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://mlsanity.com/paper/2110.03269/) (2021)
- [Graph Component Contrastive Learning for Concept Relatedness Estimation](https://mlsanity.com/paper/2206.12556/) (2022)
- [Improving Task Generalization via Unified Schema Prompt](https://mlsanity.com/paper/2208.03229/) (2022)
- [A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://mlsanity.com/paper/2210.08355/) (2022)
- [Can we obtain significant success in RST discourse parsing by using Large Language Models?](https://mlsanity.com/paper/2403.05065/) (2024)
- [Reasoning over Logically Interacted Conditions for Question Answering](https://mlsanity.com/paper/2205.12898/) (2022)
- [Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations](https://mlsanity.com/paper/2306.10658/) (2023)
- [Discourse-Aware Graph Networks for Textual Logical Reasoning](https://mlsanity.com/paper/2207.01450/) (2022)
- [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)
- [Transform, Contrast and Tell: Coherent Entity-Aware Multi-Image Captioning](https://mlsanity.com/paper/2302.02124/) (2023)
- [Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension](https://mlsanity.com/paper/2306.12069/) (2023)
- [Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading](https://mlsanity.com/paper/2310.13409/) (2023)
- [Unsupervised Technique To Conversational Machine Reading](https://mlsanity.com/paper/2106.15247/) (2021)

## Abstract (arXiv)

Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding for both document and dialog. Specifically, we split the document into clause-like elementary discourse units (EDU) using a pre-trained discourse segmentation model, and we train our model in a weakly-supervised manner to predict whether each EDU is entailed by the user feedback in a conversation. Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information. Our experiments on the ShARC benchmark (blind, held-out test set) show that Discern achieves state-of-the-art results of 78.3% macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question generation. Code and models are released at https://github.com/Yifan-Gao/Discern.