Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model

By Wenlin Zhuang, Ernie Chang. 2017

16 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hui Su
2. Xiaoyu Shen
3. Vera Demberg
4. D. Zhu
5. Ernie Chang

In Natural Language Processing

1. Hui Su
2. Xiaoyu Shen
3. Vera Demberg
4. D. Zhu
5. Ernie Chang

Citations in the corpus

• Neural Data-to-Text Generation with LM-based Text Augmentation (2021, methodology): “For future works, we hope to apply the techniques to annotation scenarios (Hong et al., 2019; de Souza et al., 2018; Zhuang and Chang, 2017; Chang et al., 2020a; Wiehr et al., 2020; Shen et al., 2020; Chang et al., 2020b; Su et al., 2020a; Chang et al., 2021a,b; Crook et al., 2018).”