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    <abstract>Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1% absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7% absolute F1 gain. We make all programs of our model publicly available for research purpose</abstract>
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%0 Conference Proceedings
%T Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
%A Shi, Ge
%A Feng, Chong
%A Huang, Lifu
%A Zhang, Boliang
%A Ji, Heng
%A Liao, Lejian
%A Huang, Heyan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shi-etal-2018-genre
%X Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1% absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7% absolute F1 gain. We make all programs of our model publicly available for research purpose
%R 10.18653/v1/D18-1125
%U https://aclanthology.org/D18-1125
%U https://doi.org/10.18653/v1/D18-1125
%P 1018-1023

Markdown (Informal)

[Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction](https://aclanthology.org/D18-1125) (Shi et al., EMNLP 2018)

• Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (Shi et al., EMNLP 2018)

ACL

• Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, and Heyan Huang. 2018. Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1018–1023, Brussels, Belgium. Association for Computational Linguistics.