论文标题
伯特了解情感吗？利用上下文嵌入之间的比较来改善基于方面的情感模型
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models
论文作者
论文摘要
当对句子中的不同单词进行极性检测时，我们需要查看周围的单词以了解情感。像Bert这样的大量训练有素的语言模型不仅可以编码文档中的单词，还可以编码围绕单词的上下文。这就提出了问题：“预熟语语言模型还自动编码有关每个单词的情感信息吗？”和“它可以用来推断出对不同方面的极性吗？”。在这项工作中，我们试图通过表明训练伯特的上下文嵌入和通用单词嵌入的比较来回答这个问题。我们还表明，如果我们对BERT和通用单词嵌入的比较构建的模型进行了捕获，则可以在基于方面的情感分类数据集中获得极性检测的最新结果。
When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment. Massively pretrained language models like BERT can encode not only just the words in a document but also the context around the words along with them. This begs the questions, "Does a pretrain language model also automatically encode sentiment information about each word?" and "Can it be used to infer polarity towards different aspects?". In this work we try to answer this question by showing that training a comparison of a contextual embedding from BERT and a generic word embedding can be used to infer sentiment. We also show that if we finetune a subset of weights the model built on comparison of BERT and generic word embedding, it can get state of the art results for Polarity Detection in Aspect Based Sentiment Classification datasets.
