文章基本信息

标题：Benchmarking Natural Language Inference and Semantic Textual Similarity for Portuguese
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作者：Pedro Fialho ; Luísa Coheur ; Paulo Quaresma 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：10
页码：484-502
DOI：10.3390/info11100484
出版社：MDPI Publishing
摘要：Two sentences can be related in many different ways. Distinct tasks in natural language processing aim to identify different semantic relations between sentences. We developed several models for natural language inference and semantic textual similarity for the Portuguese language. We took advantage of pre-trained models (BERT); additionally, we studied the roles of lexical features. We tested our models in several datasets—ASSIN, SICK-BR and ASSIN2—and the best results were usually achieved with ptBERT-Large, trained in a Brazilian corpus and tuned in the latter datasets. Besides obtaining state-of-the-art results, this is, to the best of our knowledge, the most all-inclusive study about natural language inference and semantic textual similarity for the Portuguese language.
关键词：natural language inference; semantic textual similarity; multilingual BERT; lexical features natural language inference ; semantic textual similarity ; multilingual BERT ; lexical features