“让我们吃奶奶”：标点符号在句子表征中重要吗？
"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?
摘要：
基于神经网络的嵌入已经成为用于创建文本的矢量表示以捕获词汇和语义相似性和不相似性的主流方法。一般来说，现有的编码方法将标点符号视为无关紧要的信息;因此，它们通常被视为预定义的标记/单词或在预处理阶段被消除。然而，标点符号可以在句子的语义中发挥重要作用，如“Let's eat \hl{,} grandma”和“Let's eat grandma”。我们假设，标点符号感知的表示模型会影响下游任务的性能。因此，我们提出了一个模型不可知的方法，结合了句法和上下文信息，以提高性能的情感分类任务。我们通过在公开数据集上进行实验来证实我们的发现，并提供案例研究，我们的模型生成了句子中标点符号的表示。
英文摘要：
Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities. In general, existing encoding methods dismiss the punctuation as insignificant information; consequently, they are routinely treated as a predefined token/word or eliminated in the pre-processing phase. However, punctuation could play a significant role in the semantics of the sentences, as in "Let's eat\hl{,} grandma" and "Let's eat grandma". We hypothesize that a punctuation-aware representation model would affect the performance of the downstream tasks. Thereby, we propose a model-agnostic method that incorporates both syntactic and contextual information to improve the performance of the sentiment classification task. We corroborate our findings by conducting experiments on publicly available datasets and provide case studies that our model generates representations with respect to the punctuation in the sentence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
