论文标题
Sbert研究意义表示：将句子嵌入到可解释的语义特征中
SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features
论文作者
论文摘要
基于S（Entence）Bert之类的大型语言模型的模型提供了有效，有效的句子嵌入，这些句子与人类相似性等级相关，但缺乏可解释性。另一方面，基于图的含义表示形式（例如，抽象含义表示，AMR）的图指标可以使两个句子相似的语义方面显式。但是，这样的指标往往会很慢，依靠解析器，并且在评分句子相似性时不会达到最先进的性能。 在这项工作中，我们通过学会诱导$ s $ s $ s $ tuctured $ s $ s $ entence bert嵌入（s $^3 $ bert）来瞄准两全其美。我们的S $^3 $ bert嵌入方式由可解释的子插件组成，这些子件强调各种语义句子特征（例如语义角色，否定或量化）。我们展示了如何）通过近似一套可解释的AMR图指标来学习将句子嵌入到语义特征中的分解；在我们的实验研究中，我们表明我们的方法提供了解释性 - 同时完全保留了神经句子嵌入的有效性和效率。
Models based on large-pretrained language models, such as S(entence)BERT, provide effective and efficient sentence embeddings that show high correlation to human similarity ratings, but lack interpretability. On the other hand, graph metrics for graph-based meaning representations (e.g., Abstract Meaning Representation, AMR) can make explicit the semantic aspects in which two sentences are similar. However, such metrics tend to be slow, rely on parsers, and do not reach state-of-the-art performance when rating sentence similarity. In this work, we aim at the best of both worlds, by learning to induce $S$emantically $S$tructured $S$entence BERT embeddings (S$^3$BERT). Our S$^3$BERT embeddings are composed of explainable sub-embeddings that emphasize various semantic sentence features (e.g., semantic roles, negation, or quantification). We show how to i) learn a decomposition of the sentence embeddings into semantic features, through approximation of a suite of interpretable AMR graph metrics, and how to ii) preserve the overall power of the neural embeddings by controlling the decomposition learning process with a second objective that enforces consistency with the similarity ratings of an SBERT teacher model. In our experimental studies, we show that our approach offers interpretability -- while fully preserving the effectiveness and efficiency of the neural sentence embeddings.
