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GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ...
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Human-like informative conversations: Better acknowledgements using conditional mutual information ...
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ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ...
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ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ...
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Conditional probing: measuring usable information beyond a baseline ...
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Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering ...
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Human-like informative conversations: Better acknowledgements using conditional mutual information ...
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Biomedical and clinical English model packages for the Stanza Python NLP library
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In: J Am Med Inform Assoc (2021)
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Stanza: A Python Natural Language Processing Toolkit for Many Human Languages ...
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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ...
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation ...
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Abstract:
Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves ... : Some of the results in the paper were incorrect ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2004.08694 https://dx.doi.org/10.48550/arxiv.2004.08694
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Finding Universal Grammatical Relations in Multilingual BERT ...
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