Entity relation classification aims to classify the semantic relationship between two marked entities in a given sentence,and plays a vital role in various natural language processing applications.However,existing stu...Entity relation classification aims to classify the semantic relationship between two marked entities in a given sentence,and plays a vital role in various natural language processing applications.However,existing studies focus on exploiting mono-lingual data in English,due to the lack of labeled data in other languages.How to effectively benefit from a richly-labeled language to help a poorly-labeled language is still an open problem.In this paper,we come up with a language adaptation framework for cross-lingual entity relation classification.The basic idea is to employ adversarial neural networks(AdvNN)to transfer feature representations from one language to another.Especially,such a language adaptation framework enables feature imitation via the competition between a sentence encoder and a rival language discriminator to generate effective representations.To verify the effectiveness of AdvNN,we introduce two kinds of adversarial structures,dual-channel AdvNN and single-channel AdvNN.Experimental results on the ACE 2005 multilingual training corpus show that our single-channel AdvNN achieves the best performance on both unsupervised and semi-supervised scenarios,yielding an improvement of 6.61%and 2.98%over the state-of-the-art,respectively.Compared with baselines which directly adopt a machine translation module,we find that both dual-channel and single-channel AdvNN significantly improve the performances(F1)of cross-lingual entity relation classification.Moreover,extensive analysis and discussion demonstrate the appropriateness and effectiveness of different parameter settings in our language adaptation framework.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61703293,61751206,and 61672368.
文摘Entity relation classification aims to classify the semantic relationship between two marked entities in a given sentence,and plays a vital role in various natural language processing applications.However,existing studies focus on exploiting mono-lingual data in English,due to the lack of labeled data in other languages.How to effectively benefit from a richly-labeled language to help a poorly-labeled language is still an open problem.In this paper,we come up with a language adaptation framework for cross-lingual entity relation classification.The basic idea is to employ adversarial neural networks(AdvNN)to transfer feature representations from one language to another.Especially,such a language adaptation framework enables feature imitation via the competition between a sentence encoder and a rival language discriminator to generate effective representations.To verify the effectiveness of AdvNN,we introduce two kinds of adversarial structures,dual-channel AdvNN and single-channel AdvNN.Experimental results on the ACE 2005 multilingual training corpus show that our single-channel AdvNN achieves the best performance on both unsupervised and semi-supervised scenarios,yielding an improvement of 6.61%and 2.98%over the state-of-the-art,respectively.Compared with baselines which directly adopt a machine translation module,we find that both dual-channel and single-channel AdvNN significantly improve the performances(F1)of cross-lingual entity relation classification.Moreover,extensive analysis and discussion demonstrate the appropriateness and effectiveness of different parameter settings in our language adaptation framework.
