Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering

• URL: http://arxiv.org/abs/2102.03741v1
• Date: Sun, 7 Feb 2021 08:15:51 GMT
• Title: Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering
• Authors: Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu
• Abstract summary: We propose a new architecture that models paragraphs as sequential data and considers multi-hop information retrieval as a kind of sequence labeling task. We evaluate our method on both full wiki and distractor subtask of HotpotQA, a public textual multi-hop QA dataset.
• Score: 32.69969157825044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them. However, in this paper, we point out the intrinsic defect of such methods. Instead, we propose a new architecture that models paragraphs as sequential data and considers multi-hop information retrieval as a kind of sequence labeling task. Specifically, we design a rewritable external memory to model the dependency among paragraphs. Moreover, a threshold gate mechanism is proposed to eliminate the distraction of noise paragraphs. We evaluate our method on both full wiki and distractor subtask of HotpotQA, a public textual multi-hop QA dataset requiring multi-hop information retrieval. Experiments show that our method achieves significant improvement over the published state-of-the-art method in retrieval and downstream QA task performance.

Related papers

• Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach [6.549143816134531]
  We propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. Experimental results on the multi-hop question-answering HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-07-18T02:19:00Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering [12.072618400000763]
  Multi-hop question answering (QA) is a challenging task requiring QA systems to perform complex reasoning over multiple documents. We propose a novel framework, From Easy to Hard (FE2H), to remove distracting information and obtain better contextual representations. FE2H divides both the document selector and reader into two stages following an easy-to-hard manner.
  arXiv  Detail & Related papers  (2022-05-24T02:33:58Z)
• 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)
• Joint Passage Ranking for Diverse Multi-Answer Retrieval [56.43443577137929]
  We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms.
  arXiv  Detail & Related papers  (2021-04-17T04:48:36Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval [117.07047313964773]
  We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions. Our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.
  arXiv  Detail & Related papers  (2020-09-27T06:12:29Z)
• Answering Any-hop Open-domain Questions with Iterative Document Reranking [62.76025579681472]
  We propose a unified QA framework to answer any-hop open-domain questions. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets.
  arXiv  Detail & Related papers  (2020-09-16T04:31:38Z)

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.