Improving Subject-Area Question Answering with External Knowledge

Pan, Xiaoman; Sun, Kai; Yu, Dian; Chen, Jianshu; Ji, Heng; Cardie, Claire; Yu, Dong

doi:10.18653/v1/d19-5804

Cited by 45 publications

(38 citation statements)

References 36 publications

(55 reference statements)

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“…This scoring function was then fused with QA models to make the final prediction. Pan et al (2019a) introduced an entity discovery and linking system to identify the most salient entities in the question and answer-options. Wikipedia abstracts of these entities are then extracted and appended to the reference documents to provide additional information.…”

Section: Related Workmentioning

confidence: 99%

Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

Ma¹,

Francis²,

Lu³

et al. 2019

Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing

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Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries. Recent approaches on such tasks show increased performance, only when models are either pre-trained with additional information or when domain-specific heuristics are used, without any special consideration regarding the knowledge resource type. In this paper, we perform a survey of recent commonsense QA methods and we provide a systematic analysis of popular knowledge resources and knowledge-integration methods, across benchmarks from multiple commonsense datasets. Our results and analysis show that attention-based injection seems to be a preferable choice for knowledge integration and that the degree of domain overlap, between knowledge bases and datasets, plays a crucial role in determining model success.

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Section: Related Workmentioning

confidence: 99%

Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

Ma¹,

Francis²,

Lu³

et al. 2019

Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing

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“…Note that some of the works discussed here transfer knowledge from external datasets into the QA task they address (Chung et al, 2017;Pan et al, 2019;Min et al, 2017;Qiu et al, 2018;Chen et al, 2017). In this work, we focus solely on the resources provided in the task itself because such compatible external resources may not be available in real-world applications of QA.…”

Section: Related Workmentioning

confidence: 99%

Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

Yadav

Bethard

Surdeanu

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We propose an unsupervised strategy for the selection of justification sentences for multihop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any supervised QA approach. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among approaches that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.

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“…Some previous work attempts to leverage structured knowledge from KBs to deal with the tasks of MRC and QA. Weissenborn et al (2017), Bauer et al (2018), Mihaylov and Frank (2018), Pan et al (2019), , follow a retrieve-then-encode paradigm, i.e., they first retrieve relevant knowledge from KBs, and only the retrieved knowledge relevant locally to the reading text will be encoded and integrated. By contrast, we leverage pre-trained KB embeddings which encode whole KBs.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

Wang

Liu

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

121

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Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context-and knowledgeaware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019). 1 * This work was done while the first author was an intern at Baidu Inc.

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Improving Subject-Area Question Answering with External Knowledge

Abstract: arXiv:1902.00993v3 [cs.CL]

Cited by 45 publications

References 36 publications

Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

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