论文标题
罗伯塔（Roberta），伯特（Bert and Distilbert）的构图和词汇语义：COQA的案例研究
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA
论文作者
论文摘要
许多NLP任务都受益于从上下文化的单词嵌入中转移知识，但是转移哪种类型的知识的情况是不完整的。本文研究了语言模型在对话问题回答（COQA）任务的上下文中涉及语言现象的类型。我们通过系统的误差分析（基本算术（计数短语），组成语义（否定和语义角色标记）以及词汇语义（Excralisal和Antonymy），我们通过系统的误差分析（计数短语），基本算术（计数短语），基本算术（计数短语）（否定词）（计数短语）和词汇模型确定了有问题的领域。当通过多任务学习增强相关语言知识时，模型的性能会提高。增强模型的集合在整个F1得分中的2.2和2.7分在最难的问题类别中的F1中提高了42.1分。结果表明，罗伯塔，伯特和德文伯特之间表示代表组成和词汇信息的能力差异。
Many NLP tasks have benefited from transferring knowledge from contextualized word embeddings, however the picture of what type of knowledge is transferred is incomplete. This paper studies the types of linguistic phenomena accounted for by language models in the context of a Conversational Question Answering (CoQA) task. We identify the problematic areas for the finetuned RoBERTa, BERT and DistilBERT models through systematic error analysis - basic arithmetic (counting phrases), compositional semantics (negation and Semantic Role Labeling), and lexical semantics (surprisal and antonymy). When enhanced with the relevant linguistic knowledge through multitask learning, the models improve in performance. Ensembles of the enhanced models yield a boost between 2.2 and 2.7 points in F1 score overall, and up to 42.1 points in F1 on the hardest question classes. The results show differences in ability to represent compositional and lexical information between RoBERTa, BERT and DistilBERT.
