Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
This work addresses the problem of measuring semantic similarity across multiple languages for natural language processing applications, but it is incremental as it builds on existing attention-based methods.
The paper tackled the cross-lingual Semantic Textual Similarity task by developing an attention-based recurrent neural network model, achieving a competitive top 6 result at SemEval 2017.
This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.