@inproceedings{yang-etal-2018-learning,
title = "Learning Semantic Textual Similarity from Conversations",
author = "Yang, Yinfei and
Yuan, Steve and
Cer, Daniel and
Kong, Sheng-yi and
Constant, Noah and
Pilar, Petr and
Ge, Heming and
Sung, Yun-Hsuan and
Strope, Brian and
Kurzweil, Ray",
editor = "Augenstein, Isabelle and
Cao, Kris and
He, He and
Hill, Felix and
Gella, Spandana and
Kiros, Jamie and
Mei, Hongyuan and
Misra, Dipendra",
booktitle = "Proceedings of the Third Workshop on Representation Learning for {NLP}",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3022",
doi = "10.18653/v1/W18-3022",
pages = "164--174",
abstract = "We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational responses. The resulting sentence embeddings perform well on the Semantic Textual Similarity (STS) Benchmark and SemEval 2017{'}s Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training, combining conversational response prediction and natural language inference. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS Benchmark and is competitive with the state-of-the-art feature engineered and mixed systems for both tasks.",
}
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%0 Conference Proceedings
%T Learning Semantic Textual Similarity from Conversations
%A Yang, Yinfei
%A Yuan, Steve
%A Cer, Daniel
%A Kong, Sheng-yi
%A Constant, Noah
%A Pilar, Petr
%A Ge, Heming
%A Sung, Yun-Hsuan
%A Strope, Brian
%A Kurzweil, Ray
%Y Augenstein, Isabelle
%Y Cao, Kris
%Y He, He
%Y Hill, Felix
%Y Gella, Spandana
%Y Kiros, Jamie
%Y Mei, Hongyuan
%Y Misra, Dipendra
%S Proceedings of the Third Workshop on Representation Learning for NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F yang-etal-2018-learning
%X We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational responses. The resulting sentence embeddings perform well on the Semantic Textual Similarity (STS) Benchmark and SemEval 2017’s Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training, combining conversational response prediction and natural language inference. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS Benchmark and is competitive with the state-of-the-art feature engineered and mixed systems for both tasks.
%R 10.18653/v1/W18-3022
%U https://aclanthology.org/W18-3022
%U https://doi.org/10.18653/v1/W18-3022
%P 164-174
Markdown (Informal)
[Learning Semantic Textual Similarity from Conversations](https://aclanthology.org/W18-3022) (Yang et al., RepL4NLP 2018)
ACL
- Yinfei Yang, Steve Yuan, Daniel Cer, Sheng-yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018. Learning Semantic Textual Similarity from Conversations. In Proceedings of the Third Workshop on Representation Learning for NLP, pages 164–174, Melbourne, Australia. Association for Computational Linguistics.