Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Mar 2017.
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Citing papers per year: 2021: 3.
3 papers.
- 2021Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learningcited by 34
- 2021Neural Data-to-Text Generation with LM-based Text Augmentationcited by 22
- 2021Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labelingcited by 11
- 2021Neural Data-to-Text Generation with LM-based Text Augmentation6 Feb 2021
- 2021Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning6 Feb 2021
- 2021Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling6 Feb 2021