Multi-Hop Paragraph Retrieval for Open-Domain Question Answering

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2296–2309 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2296 Multi-Hop Paragraph Retrieval for Open-Domain Question Answering Yair Feldman and Ran El-Yaniv Department of Computer Science Technion – Israel Institute of Technology Haifa, Israel {yairf11, rani}@cs.technion.ac.il Abstract This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting para- graphs by forming a joint vector representa- tion of both a question and a paragraph. The retrieval is performed by considering contex- tualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, re- spectively. 1 1 Introduction Textual Question Answering (QA) is the task of answering natural language questions given a set of contexts from which the answers to these ques- tions can be inferred. This task, which falls un- der the domain of natural language understand- ing, has been attracting massive interest due to ex- tremely promising results that were achieved us- ing deep learning techniques. These results were made possible by the recent creation of a variety of large-scale QA datasets, such as TriviaQA (Joshi et al., 2017) and SQuAD (Rajpurkar et al., 2016). The latest state-of-the-art methods are even capa- ble of outperforming humans on certain tasks (De- vlin et al., 2018)2. The basic and arguably the most popular task of QA is often referred to as Reading Comprehension 1Code is available at https://github.com/yairf11/MUPPET 2https://rajpurkar.github.io/SQuAD-explorer/ (RC), in which each question is paired with a rela- tively small number of paragraphs (or documents) from which the answer can potentially be inferred. The objective in RC is to extract the correct an- swer from the given contexts or, in some cases, deem the question unanswerable (Rajpurkar et al., 2018). Most large-scale RC datasets, however, are built in such a way that the answer can be inferred using a single paragraph or document. This kind of reasoning is termed single-hop reasoning, since it requires reasoning over a single piece of evi- dence. A more challenging task, called multi-hop reasoning, is one that requires combining evidence from multiple sources (Talmor and Berant, 2018; Welbl et al., 2018; Yang et al., 2018). Figure 1 provides an example of a question requiring multi- hop reasoning. To answer the question, one must first infer from the first context that Alex Ferguson is the manager in question, and only then can the answer to the question be inferred with any confi- dence from the second context. Another setting for QA is open-domain QA, in which questions are given without any accompa- nying contexts, and one is required …

Content truncated to 500 words