TY - GEN
T1 - Interactive attention networks for semantic text matching
AU - Zhao, Sendong
AU - Huang, Yong
AU - Su, Chang
AU - Li, Yuantong
AU - Wang, Fei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Semantic text matching, which matches target texts to source texts, is a general problem in many areas, such as information retrieval, question answering, and recommendation. The challenges to existing research on this topic include 1) out-of-vocabulary and low-frequency keywords and 2) direct utilization of sparse matching matrix of source and target. The out-of-vocabulary and low-frequency keywords could lead to the mismatch of similar keywords in source and target texts. The sparse matching matrix cannot provide enough clues to match the source with the target. To address these challenges, we propose a novel deep neural semantic text matching model. Our model adopts an interactive attention network to achieve information exchange between the source text and the target text, and dynamically explores the matching matrix and learns new representations of source and target texts. Experimental results on three different text matching datasets demonstrate that our model can significantly outperform competitive baselines. Furthermore, our model demonstrates great advantage in alleviating the sparse matching problem and learning out-of-vocabulary words with the local context, which widely exists in a broad spectrum of NLP applications.
AB - Semantic text matching, which matches target texts to source texts, is a general problem in many areas, such as information retrieval, question answering, and recommendation. The challenges to existing research on this topic include 1) out-of-vocabulary and low-frequency keywords and 2) direct utilization of sparse matching matrix of source and target. The out-of-vocabulary and low-frequency keywords could lead to the mismatch of similar keywords in source and target texts. The sparse matching matrix cannot provide enough clues to match the source with the target. To address these challenges, we propose a novel deep neural semantic text matching model. Our model adopts an interactive attention network to achieve information exchange between the source text and the target text, and dynamically explores the matching matrix and learns new representations of source and target texts. Experimental results on three different text matching datasets demonstrate that our model can significantly outperform competitive baselines. Furthermore, our model demonstrates great advantage in alleviating the sparse matching problem and learning out-of-vocabulary words with the local context, which widely exists in a broad spectrum of NLP applications.
KW - Deep neural networks
KW - Information retrieval
KW - Interactive attention
KW - Out-of-vocabulary words
KW - Question answering
KW - Sparse matching
KW - Text semantic matching
KW - Tweet linking
UR - https://www.scopus.com/pages/publications/85100890799
U2 - 10.1109/ICDM50108.2020.00095
DO - 10.1109/ICDM50108.2020.00095
M3 - 会议稿件
AN - SCOPUS:85100890799
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 861
EP - 870
BT - Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
A2 - Plant, Claudia
A2 - Wang, Haixun
A2 - Cuzzocrea, Alfredo
A2 - Zaniolo, Carlo
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Data Mining, ICDM 2020
Y2 - 17 November 2020 through 20 November 2020
ER -