DeepAI AI Chat
Log In Sign Up

Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games

by   Yufei Feng, et al.

We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i.e., the question-answer pairs. We propose a cooperative game approach to deal with this problem, in which how the evidence passages are selected and how the selected passages are connected are handled by two models that cooperate to select the most confident chains from a large set of candidates (from distant supervision). For evaluation, we created benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and hand-labeled reasoning chains for the latter. The experimental results demonstrate the effectiveness of our proposed approach.


page 1

page 2

page 3

page 4


Exploiting Reasoning Chains for Multi-hop Science Question Answering

We propose a novel Chain Guided Retriever-reader (CGR) framework to mode...

Robustifying Multi-hop QA through Pseudo-Evidentiality Training

This paper studies the bias problem of multi-hop question answering mode...

MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning

Multi-hop reasoning approaches over knowledge graphs infer a missing rel...

Do Multi-hop Readers Dream of Reasoning Chains?

General Question Answering (QA) systems over texts require the multi-hop...

Multi-hop Question Answering via Reasoning Chains

Multi-hop question answering requires models to gather information from ...

Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

We introduce the task of implicit offensive text detection in dialogues,...

Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning

Our goal is a question-answering (QA) system that can show how its answe...