Reference Knowledgeable Network for Machine Reading Comprehension

• URL: http://arxiv.org/abs/2012.03709v1
• Date: Mon, 7 Dec 2020 14:11:33 GMT
• Title: Reference Knowledgeable Network for Machine Reading Comprehension
• Authors: Yilin Zhao, Zhuosheng Zhang, Hai Zhao
• Abstract summary: Multi-choice Machine Reading (MRC) is a major and challenging form of MRC tasks. We propose a novel reference-based knowledge enhancement model based on span extraction called Reference Knowledgeable Network (RekNet) In detail, RekNet refines fine-grained critical information and defines it as Reference Span, then quotes external knowledge quadruples by the co-occurrence information of Reference Span and answer options.
• Score: 43.352833140317486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-choice Machine Reading Comprehension (MRC) is a major and challenging form of MRC tasks that requires model to select the most appropriate answer from a set of candidates given passage and question. Most of the existing researches focus on the modeling of the task datasets without explicitly referring to external fine-grained commonsense sources, which is a well-known challenge in multi-choice tasks. Thus we propose a novel reference-based knowledge enhancement model based on span extraction called Reference Knowledgeable Network (RekNet), which simulates human reading strategy to refine critical information from the passage and quote external knowledge in necessity. In detail, RekNet refines fine-grained critical information and defines it as Reference Span, then quotes external knowledge quadruples by the co-occurrence information of Reference Span and answer options. Our proposed method is evaluated on two multi-choice MRC benchmarks: RACE and DREAM, which shows remarkable performance improvement with observable statistical significance level over strong baselines.

Related papers

• Teaching Smaller Language Models To Generalise To Unseen Compositional Questions (Full Thesis) [0.0]
  We train our models to answer diverse questions by instilling an ability to reason over a retrieved context. We acquire context from two knowledge sources; a Wikipedia corpus queried using a multi-hop dense retrieval system with novel extensions, and from rationales generated from a larger Language Model optimised to run in a lower resource environment.
  arXiv  Detail & Related papers  (2024-11-25T23:25:34Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering [27.601353412882258]
  Multi-choice Machine Reading (MMRC) aims to select the correct answer from a set of options based on a given passage and question. In this paper, we reconstruct multi-choice to single-choice by training a binary classification to distinguish whether a certain answer is correct. Our proposed method gets rid of the multi-choice framework and can leverage resources of other tasks.
  arXiv  Detail & Related papers  (2024-04-27T16:02:55Z)
• Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling [96.75821232222201]
  Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. We propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting.
  arXiv  Detail & Related papers  (2023-05-19T14:56:57Z)
• MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization [55.60306377044225]
  State-of-the-art summarization systems can generate highly fluent summaries. These summaries, however, may contain factual inconsistencies and/or information not present in the source. We introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared.
  arXiv  Detail & Related papers  (2023-01-28T23:08:25Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)
• Retrospective Reader for Machine Reading Comprehension [90.6069071495214]
  Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder. This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions.
  arXiv  Detail & Related papers  (2020-01-27T11:14:34Z)

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.