Enhancing inter-sentence attention for Semantic Textual Similarity

Semantic Textual Similarity (STS) is a fundamental task that aims to measure semantic equiva-lence between two sentences. The pre-trained language models based on Transformer have had great success on the STS task, which is characterized by multi-head self-attention. However, inter-sentence attention referring to attention between a pair of sentences has not been extensively explored or studied, despite its great value to the STS task. To guide models to focus more attentionon inter-sentence information, such as synonyms, hyponyms, meronyms and antonyms between sentences, we propose a novel multi-head self-attention architecture, namely Enhanced Inter-sentence Attention (EIA). Specifically, EIA combines the enhanced inter-sentence attention with the original attention by a gated fusion module based on Transformer. The architecture effectively integrating the essential information from both attention. Experiments on benchmark datasets verify the effectiveness of our proposed architecture, as the performance of EIA based on RoBERTa reaches 91.22% (Pearson Correlation) and 86.92% (Spearman Correlation) on the SICK dataset, which are 0.52%-2.15% higher than other strong baseline models. Moreover, our EIA can be successfully integrated with other modified transformer-based models to further improve their performance, demonstrating its ability to effectively model matching information between sentences.

Automated summary

This paper presents an Enhanced Inter-sentence Attention (EIA) architecture to improve the task of semantic textual similarity (STS). The architecture integrates enhanced inter-sentence attention with original attention through a gated fusion module, effectively incorporating important inter-sentence information. Experiments show that EIA achieves improved performance on benchmark datasets.