Prompt-based Conservation Learning for Multi-hop Question Answering

• URL: http://arxiv.org/abs/2209.06923v1
• Date: Wed, 14 Sep 2022 20:50:46 GMT
• Title: Prompt-based Conservation Learning for Multi-hop Question Answering
• Authors: Zhenyun Deng, Yonghua Zhu, Yang Chen, Qianqian Qi, Michael Witbrock, Patricia Riddle
• Abstract summary: Multi-hop question answering requires reasoning over multiple documents to answer a complex question. Most existing multi-hop QA methods fail to answer a large fraction of sub-questions. We propose the Prompt-based Conservation Learning framework for multi-hop QA.
• Score: 11.516763652013005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a model has performed the desired reasoning to reach the correct answer. Most existing multi-hop QA methods fail to answer a large fraction of sub-questions, even if their parent questions are answered correctly. In this paper, we propose the Prompt-based Conservation Learning (PCL) framework for multi-hop QA, which acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop QA tasks, mitigating forgetting. Specifically, we first train a model on existing single-hop QA tasks, and then freeze this model and expand it by allocating additional sub-networks for the multi-hop QA task. Moreover, to condition pre-trained language models to stimulate the kind of reasoning required for specific multi-hop questions, we learn soft prompts for the novel sub-networks to perform type-specific reasoning. Experimental results on the HotpotQA benchmark show that PCL is competitive for multi-hop QA and retains good performance on the corresponding single-hop sub-questions, demonstrating the efficacy of PCL in mitigating knowledge loss by forgetting.

Related papers

• Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering [85.79940770146557]
  We decompose multi-hop questions into multiple corresponding single-hop questions. We find marked inconsistency in QA models' answers on these pairs of ostensibly identical question chains. When trained only on single-hop questions, models generalize poorly to multi-hop questions.
  arXiv  Detail & Related papers  (2022-10-09T11:48:07Z)
• Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering [71.49131159045811]
  Multi-hop reasoning requires aggregating multiple documents to answer a complex question. Existing methods usually decompose the multi-hop question into simpler single-hop questions. We propose an interpretable stepwise reasoning framework to incorporate both single-hop supporting sentence identification and single-hop question generation.
  arXiv  Detail & Related papers  (2022-08-22T13:24:25Z)
• Modeling Multi-hop Question Answering as Single Sequence Prediction [88.72621430714985]
  We propose a simple generative approach (PathFid) that extends the task beyond just answer generation. PathFid explicitly models the reasoning process to resolve the answer for multi-hop questions. Our experiments demonstrate that PathFid leads to strong performance gains on two multi-hop QA datasets.
  arXiv  Detail & Related papers  (2022-05-18T21:57:59Z)
• Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes [14.302797773412543]
  Multi-hop Question Answering (QA) is a challenging task since it requires an accurate aggregation of information from multiple context paragraphs. Recent work in multi-hop QA has shown that performance can be boosted by first decomposing the questions into simpler, single-hop questions. We show that decomposition is an effective form of probing QA systems as well as a promising approach to explanation generation.
  arXiv  Detail & Related papers  (2022-04-16T01:03:36Z)
• Ask to Understand: Question Generation for Multi-hop Question Answering [11.626390908264872]
  Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. We propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG)
  arXiv  Detail & Related papers  (2022-03-17T04:02:29Z)
• Unsupervised Multi-hop Question Answering by Question Generation [108.61653629883753]
  MQA-QG is an unsupervised framework that can generate human-like multi-hop training data. Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance.
  arXiv  Detail & Related papers  (2020-10-23T19:13:47Z)
• Reinforced Multi-task Approach for Multi-hop Question Generation [47.15108724294234]
  We take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA.
  arXiv  Detail & Related papers  (2020-04-05T10:16:59Z)
• Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? [23.991872322492384]
  We investigate whether top-performing models for multi-hop questions understand the underlying sub-questions like humans. We show that multiple state-of-the-art multi-hop QA models fail to correctly answer a large portion of sub-questions. Our work takes a step forward towards building a more explainable multi-hop QA system.
  arXiv  Detail & Related papers  (2020-02-23T15:16:43Z)
• Unsupervised Question Decomposition for Question Answering [102.56966847404287]
  We propose an algorithm for One-to-N Unsupervised Sequence Sequence (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions. We show large QA improvements on HotpotQA over a strong baseline on the original, out-of-domain, and multi-hop dev sets.
  arXiv  Detail & Related papers  (2020-02-22T19:40:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.