论文标题
sentibert：一种可转移的基于变压器的结构，用于构图情感语义
SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
论文作者
论文摘要
我们提出了Sentibert，这是BERT的一种变体，可有效捕获作曲情感语义。该模型将上下文化表示与二进制选区解析树结合起来，以捕获语义组成。全面的实验表明，Sentibert在短语级别的情感分类上实现了竞争性能。我们进一步证明，从SST上的短语级注释中学到的情感构成可以转移到其他情感分析任务以及相关任务，例如情感分类任务。此外，我们进行消融研究和设计可视化方法来了解Sentiblet。我们表明，Sentibert在捕获否定和对比关系并建模组成情感语义方面比基线方法更好。
We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics. The model incorporates contextualized representation with binary constituency parse tree to capture semantic composition. Comprehensive experiments demonstrate that SentiBERT achieves competitive performance on phrase-level sentiment classification. We further demonstrate that the sentiment composition learned from the phrase-level annotations on SST can be transferred to other sentiment analysis tasks as well as related tasks, such as emotion classification tasks. Moreover, we conduct ablation studies and design visualization methods to understand SentiBERT. We show that SentiBERT is better than baseline approaches in capturing negation and the contrastive relation and model the compositional sentiment semantics.
