Multi-hop Question Answering

IF 8.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Foundations and Trends in Information Retrieval Pub Date : 2024-06-12 DOI:10.1561/1500000102
Vaibhav Mavi, Anubhav Jangra, Jatowt Adam
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Abstract

The task of Question Answering (QA) has attracted significant research interest for a long time. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting, makes the task of QA crucial for strong AI systems. Recent success on simple QA tasks has shifted the focus to more complex settings. Among these, Multi-Hop QA (MHQA) is one of the most researched tasks over recent years. In broad terms, MHQA is the task of answering natural language questions that involve extracting and combining multiple pieces of information and doing multiple steps of reasoning. An example of a multi-hop question would be “The Argentine PGA Championship record holder has won how many tournaments worldwide?”. Answering the question would need two pieces of information: “Who is the record holder for Argentine PGA Championship tournaments?” and “How many tournaments did [Answer of Sub Q1] win?”. The ability to answer multi-hop questions and perform multi step reasoning can significantly improve the utility of NLP systems. Consequently, the field has seen a surge of high quality datasets, models and evaluation strategies. The notion of ‘multiple hops’ is somewhat abstract which results in a large variety of tasks that require multihop reasoning. This leads to different datasets and models that differ significantly from each other and make the field challenging to generalize and survey. We aim to provide a general and formal definition of the MHQA task, and organize and summarize existing MHQA frameworks. We also outline some best practices for building MHQA datasets. This monograph provides a systematic and thorough introduction as well as the structuring of the existing attempts to this highly interesting, yet quite challenging task.

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多跳问题解答
长期以来,问题解答(QA)任务一直备受研究关注。它与语言理解和知识检索任务的相关性以及简单的设置,使得 QA 任务对强大的人工智能系统至关重要。最近,在简单的质量保证任务上取得的成功将焦点转移到了更复杂的环境上。其中,多跳 QA(MHQA)是近年来研究最多的任务之一。从广义上讲,MHQA 是回答自然语言问题的任务,这些问题涉及提取和组合多种信息并进行多步推理。多跳问题的一个例子是 "阿根廷 PGA 锦标赛纪录保持者赢得了多少场全球锦标赛?回答这个问题需要两条信息:"阿根廷 PGA 锦标赛纪录保持者是谁?"和"[子问题 1 的答案]赢得了多少场锦标赛?"。回答多跳问题和执行多步推理的能力可以显著提高 NLP 系统的实用性。因此,该领域涌现出了大量高质量的数据集、模型和评估策略。"多跳 "的概念有些抽象,这导致需要多跳推理的任务种类繁多。这导致不同的数据集和模型之间存在很大差异,给该领域的推广和调查带来了挑战。我们的目标是提供 MHQA 任务的一般和正式定义,并整理和总结现有的 MHQA 框架。本专著系统而全面地介绍了这一非常有趣但又颇具挑战性的任务,并对现有的尝试进行了结构化。
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来源期刊
Foundations and Trends in Information Retrieval
Foundations and Trends in Information Retrieval COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
39.10
自引率
0.00%
发文量
3
期刊介绍: The surge in research across all domains in the past decade has resulted in a plethora of new publications, causing an exponential growth in published research. Navigating through this extensive literature and staying current has become a time-consuming challenge. While electronic publishing provides instant access to more articles than ever, discerning the essential ones for a comprehensive understanding of any topic remains an issue. To tackle this, Foundations and Trends® in Information Retrieval - FnTIR - addresses the problem by publishing high-quality survey and tutorial monographs in the field. Each issue of Foundations and Trends® in Information Retrieval - FnT IR features a 50-100 page monograph authored by research leaders, covering tutorial subjects, research retrospectives, and survey papers that provide state-of-the-art reviews within the scope of the journal.
期刊最新文献
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