Attentive Siamese LSTM network for semantic textual similarity measure
Bao, Wei, Bao, Wugedele, Du, JinhuaORCID: 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
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.
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