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DAGN: Discourse-Aware Graph Network for Logical Reasoning

arXiv 2103.14349 ↗First posted 26 Mar 2021cs.CLcited by 28

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Abstract (arXiv)

Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting among tokens. In this work, we explore aggregating passage-level clues for solving logical reasoning QA by using discourse-based information. We propose a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts. The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks. Experiments are conducted on two logical reasoning QA datasets, ReClor and LogiQA, and our proposed DAGN achieves competitive results. The source code is available at https://github.com/Eleanor-H/DAGN.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 35 of its 43 references that are in the map. First posted on arXiv 26 Mar 2021.

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35 of 43 references.

References · 35

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23 more
  1. 2019Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoningcited by 281
  2. 2020Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learningcited by 216
  3. 2020ReClor: A Reading Comprehension Dataset Requiring Logical Reasoningcited by 211
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Citing papers per year: 2021: 4; 2022: 12; 2023: 10; 2024: 1; 2025: 1.

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  1. 2021Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Textcited by 46
  2. 2022Reasoning Like Program Executorscited by 45
  3. 2021From LSAT: The Progress and Challenges of Complex Reasoningcited by 43
  4. 2022APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversationscited by 23
  5. 2022MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoningcited by 22
  6. 2022A Survey for Efficient Open Domain Question Answeringcited by 20
  7. 2022TASA: Deceiving Question Answering Models by Twin Answer Sentences Attackcited by 15
  8. 2023DisCGen: A Framework for Discourse-Informed Counterspeech Generationcited by 14
  9. 2022Towards Fine-grained Causal Reasoning and QAcited by 13
  10. 2023GLoRE: Evaluating Logical Reasoning of Large Language Modelscited by 13
  11. 2021BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddlescited by 10
  12. 2022AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehensioncited by 8
16 more
  1. 2022Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoningcited by 8
  2. 2023Exploring Self-supervised Logic-enhanced Training for Large Language Modelscited by 8
  3. 2023Certified Deductive Reasoning with Language Modelscited by 8
  4. 2023CLOMO: Counterfactual Logical Modification with Large Language Modelscited by 7
  5. 2022APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoningcited by 5
  6. 2021Fact-driven Logical Reasoning for Machine Reading Comprehensioncited by 4
  7. 2022MetaLogic: Logical Reasoning Explanations with Fine-Grained Structurecited by 3
  8. 2023IDOL: Indicator-oriented Logic Pre-training for Logical Reasoningcited by 3
  9. 2022Discourse-Aware Graph Networks for Textual Logical Reasoningcited by 2
  10. 2023Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Textcited by 2
  11. 2023Out-of-Distribution Generalization in Text Classification: Past, Present, and Futurecited by 1
  12. 2023Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehensioncited by 1
  13. 2022elBERto: Self-supervised Commonsense Learning for Question Answeringcited by 0
  14. 2023Proposition from the Perspective of Chinese Language: A Chinese Proposition Classification Evaluation Benchmarkcited by 0
  15. 2024Unsupervised multiple choices question answering via universal corpuscited by 0
  16. 2025Chaining Event Spans for Temporal Relation Groundingcited by 0