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Attentive Siamese LSTM network for semantic textual similarity measure

Bao, Wei, Bao, Wugedele, Du, Jinhua orcid logoORCID: 0000-0002-3267-4881, Yang, Yuanyuan and Zhao, Xiaobing (2019) Attentive Siamese LSTM network for semantic textual similarity measure. In: 2018 International Conference on Asian Language Processing (IALP), 15-18 Nov 2018, Bandung, Indonesia. ISBN 978-1-7281-1175-9

Abstract
Semantic Textual Similarity (STS) is important for many applications such as Plagiarism Detection (PD), Text Paraphrasing and Information Retrieval (IR). Current methods for STS rely on statistical machine learning. Recent studies showed that neural networks for STS presented promising experimental results. In this paper, we propose an Attentive Siamese Long Short-Term Memory (LSTM) network for measuring Semantic Textual Similarity. Instead of external resources and handcraft features, raw sentence pairs and pre-trained word embedding are needed as input. Attention mechanism is utilized in LSTM network to capture high-level semantic information. We demonstrated the effectiveness of our model by applying the architecture in different tasks: three corpora and three language tasks. Experimental results on all tasks and languages show that our method with attention mechanism outperforms the baseline model with a higher correlation with human annotation.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:semantic textual similarity; attention mechanism; siamese LSTM
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: 2018 International Conference on Asian Language Processing (IALP), Proccedings of. . IEEE. ISBN 978-1-7281-1175-9
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/IALP.2018.8629212
Copyright Information:© 2018 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:National Natural Science Foundation of China (NSFC) (No.61331013,No.61501529), National Language Commission (ZDI135-39), National Social Science Foundation (No.17CYY044)
ID Code:23328
Deposited On:20 May 2019 14:51 by Thomas Murtagh . Last Modified 20 May 2019 14:51
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