DAGN: Discourse-Aware Graph Network for Logical Reasoning
By Yinya Huang, Meng Fang, Yu Cao, Liwei Wang, Xiaodan Liang. 2021
69 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing, Prompting & Reasoning
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Citations in the corpus
- Reasoning Like Program Executors (2022, methodology): “(v) On LogiQA , we compare our methods with Co-Matching Network (Wang et al., 2018c) and the SOTA model DAGN (Huang et al., 2021).”
- Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2021, methodology): “, 2020) are usually employed for converting texts to logical forms, and graph neural networks (Fang et al., 2019; Huang et al., 2021) and neural module networks (Gupta et al.”
- Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning (2021, background): “This does not mean that the tasks would be unsolvable in general: Several studies [Huang et al., 2021, Ouyang et al., 2021, Wang et al., 2021b, Yang et al., 2020, Lourie et al., 2021, Xu et al., 2021] show that the addition of symbolic knowledge (such as date normalization, quasi-logical reasoning,…”
- From LSAT: The Progress and Challenges of Complex Reasoning (2021, background): “They then utilize a neural module to learn continuous vectors for symbolic representations to deal with the uncertainty of data [75, 76, 20].”