Exploiting Reasoning Chains for Multi-hop Science Question Answering
By Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam. 2021
32 citations recorded by the source corpus.
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A \textit{Chain-aware loss}, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.
Subfields: Artificial Intelligence, Natural Language Processing, Prompting & Reasoning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Natural Language Processing
- Denny Zhou
- Jason Wei
- Dale Schuurmans
- Ed H. Chi
- Xuezhi Wang
- Dale Schuurmans
- André Freitas
- Mokanarangan Thayaparan
In Prompting & Reasoning
- Denny Zhou
- Xuezhi Wang
- Jason Wei
- Ed H. Chi
- Quoc V. Le
- Wenhu Chen
- Dale Schuurmans
- Mokanarangan Thayaparan
Citations in the corpus
- Self-Consistency Improves Chain of Thought Reasoning in Language Models (2022, methodology): “Some previous work has considered task-specific approaches for identifying reasoning paths, such as constructing semantic graphs (Xu et al., 2021a), learning an RNN to retrieve reasoning paths over the Wikipedia graph (Asai et al., 2020), fine-tuning with human annotated reasoning paths on math…”
- Rationale-Augmented Ensembles in Language Models (2022, methodology): “One relevant line of work tries to improve ratio-nalization and interpretability in natural language processing models, for example, by extracting rationales using task-specific approaches (Xu et al., 2021; Asai et al., 2020; Chen et al., 2019).”
- Case-Based Abductive Natural Language Inference (2020, background): “The work presented in this paper is related to hybrid neuro-symbolic approaches for inference with natural language (Liu et al., 2020; Minervini et al., 2020; Jiang and Bansal, 2019; Chen et al., 2019; Dua et al., 2019; Xu et al., 2021; Weber et al., 2019).”