TY - GEN
T1 - An adversarial joint learning model for low-resource language semantic textual similarity
AU - Tian, Junfeng
AU - Lan, Man
AU - Wu, Yuanbin
AU - Wang, Jingang
AU - Qiu, Long
AU - Li, Sheng
AU - Jun, Lang
AU - Si, Luo
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Semantic Textual Similarity (STS) of low-resource language is a challenging research problem with practical applications. Traditional solutions employ machine translation techniques to translate the low-resource languages to some resource-rich languages such as English. Hence, the final performance is highly dependent on the quality of machine translation. To decouple the machine translation dependency while still take advantage of the data in resource-rich languages, this work proposes to jointly learn the low-resource language STS task and that of a resource-rich one, which only relies on multilingual word embeddings. In particular, we project the low-resource language word embeddings into the semantic space of the resource-rich language via a translation matrix. To make the projected word embeddings resemble that of the resource-rich language, a language discriminator is introduced as an adversarial teacher. Thus the parameters of sentence similarity neural networks of two tasks can be effectively shared. The plausibility of our model is demonstrated by extensive experimental results.
AB - Semantic Textual Similarity (STS) of low-resource language is a challenging research problem with practical applications. Traditional solutions employ machine translation techniques to translate the low-resource languages to some resource-rich languages such as English. Hence, the final performance is highly dependent on the quality of machine translation. To decouple the machine translation dependency while still take advantage of the data in resource-rich languages, this work proposes to jointly learn the low-resource language STS task and that of a resource-rich one, which only relies on multilingual word embeddings. In particular, we project the low-resource language word embeddings into the semantic space of the resource-rich language via a translation matrix. To make the projected word embeddings resemble that of the resource-rich language, a language discriminator is introduced as an adversarial teacher. Thus the parameters of sentence similarity neural networks of two tasks can be effectively shared. The plausibility of our model is demonstrated by extensive experimental results.
KW - Adversarial learning
KW - Low-resource language
KW - Neural networks
KW - Semantic textual similarity
UR - https://www.scopus.com/pages/publications/85044465189
U2 - 10.1007/978-3-319-76941-7_7
DO - 10.1007/978-3-319-76941-7_7
M3 - 会议稿件
AN - SCOPUS:85044465189
SN - 9783319769400
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 89
EP - 101
BT - Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings
A2 - Azzopardi, Leif
A2 - Pasi, Gabriella
A2 - Hanbury, Allan
A2 - Piwowarski, Benjamin
PB - Springer Verlag
T2 - 40th European Conference on Information Retrieval, ECIR 2018
Y2 - 26 March 2018 through 29 March 2018
ER -