A semantics-aware approach for multilingual natural language inference.
This paper introduces a semantics-aware approach to natural language inference which allows neural network models to perform better on natural language inference benchmarks. We propose to incorporate explicit lexical and concept-level semantics from knowledge bases to improve inference accuracy. We...
| Publicado en: | Language Resources & Evaluation Vol. 57; no. 2; pp. 611 - 640 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=163826593&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 163826593 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2023 vid: 57 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 163826593 10.1007/s10579-023-09635-6 ppf: 611 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: A semantics-aware approach for multilingual natural language inference. aug: au: Le-Hong, Phuong Cambria, Erik affil: Vietnam National University, Hanoi, Vietnam School of Computer Science and Engineering, NTU, Singapore, Singapore su: Convolutional neural networks Natural languages Transformer models Inference (Logic) Artificial neural networks Recurrent neural networks sug: subj: Convolutional neural networks Natural languages Transformer models Inference (Logic) Artificial neural networks Recurrent neural networks keyword: Commonsense Language inference Semantics Text Transformers ab: This paper introduces a semantics-aware approach to natural language inference which allows neural network models to perform better on natural language inference benchmarks. We propose to incorporate explicit lexical and concept-level semantics from knowledge bases to improve inference accuracy. We conduct an extensive evaluation of four models using different sentence encoders, including continuous bag-of-words, convolutional neural network, recurrent neural network, and the transformer model. Experimental results demonstrate that semantics-aware neural models give better accuracy than those without semantics information. On average of the three strong models, our semantic-aware approach improves natural language inference in different languages. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2023. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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