Dependency or Span, End-to-End Uniform Semantic Role Labeling

Li, Zuchao; He, Shexia; Zhang, Hai; Zhang, Yiqing; Zhang, Zhuosheng; Zhou, Xi; Zhou, Xiang

doi:10.1609/aaai.v33i01.33016730

Cited by 85 publications

(66 citation statements)

References 15 publications

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“…built a full end-to-end SRL model with biaffine attention and provided strong performance on English and Chinese. Li et al (2019) also proposed an end-to-end model for both dependency and span SRL with a unified argument representation, obtaining favorable results on English.…”

Section: Related Workmentioning

confidence: 99%

Syntax-aware Multilingual Semantic Role Labeling

Zhao

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation. However, most of these efforts focus on English, while SRL on multiple languages more than English has received relatively little attention so that is kept underdevelopment. Thus this paper intends to fill the gap on multilingual SRL with special focus on the impact of syntax and contextualized word representation. Unlike existing work, we propose a novel method guided by syntactic rule to prune arguments, which enables us to integrate syntax into multilingual SRL model simply and effectively. We present a unified SRL model designed for multiple languages together with the proposed uniform syntax enhancement. Our model achieves new state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. Besides, we pose a discussion on the syntactic role among different languages and verify the effectiveness of deep enhanced representation for multilingual SRL.

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Section: Related Workmentioning

confidence: 99%

Syntax-aware Multilingual Semantic Role Labeling

Zhao

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…In future work, we will further explore the effectiveness of the reordering mechanism and apply it to other natural language processing tasks, such dependency parsing , and semantic role labeling Li et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Neural Machine Translation with Reordering Embeddings

Chen¹,

Wang²,

Utiyama³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information. These reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation. The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system. Experimental results on WMT'14 English-to-German, NIST Chinese-to-English, and WAT ASPEC Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer translation system.

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“…The supervised BWE (Mikolov et al, 2013), which exploits similarities between the source language and the target language through a linear transformation matrix, serves as the basis for many NLP tasks, such as machine translation (Bahdanau et al, 2015;Vaswani et al, 2017;Chen et al, 2018b;, dependency parsing , semantic role labeling Li et al, 2019). However, the lack of a large wordpair dictionary poses a major practical problem for many language pairs.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation

Sun

Wang

Chen

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Unsupervised bilingual word embedding (UBWE), together with other technologies such as back-translation and denoising, has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. In previous methods, UBWE is first trained using nonparallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT. That is, the training of UBWE and UNMT are separate. In this paper, we first empirically investigate the relationship between UBWE and UNMT. The empirical findings show that the performance of UNMT is significantly affected by the performance of UBWE. Thus, we propose two methods that train UNMT with UBWE agreement. Empirical results on several language pairs show that the proposed methods significantly outperform conventional UNMT.

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Dependency or Span, End-to-End Uniform Semantic Role Labeling

Cited by 85 publications

References 15 publications

Syntax-aware Multilingual Semantic Role Labeling

Syntax-aware Multilingual Semantic Role Labeling

Neural Machine Translation with Reordering Embeddings

Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation

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