Kevin Parnow


2021

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Grammatical Error Correction as GAN-like Sequence Labeling
Kevin Parnow | Zuchao Li | Hai Zhao
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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MiSS: An Assistant for Multi-Style Simultaneous Translation
Zuchao Li | Kevin Parnow | Masao Utiyama | Eiichiro Sumita | Hai Zhao
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this paper, we present MiSS, an assistant for multi-style simultaneous translation. Our proposed translation system has five key features: highly accurate translation, simultaneous translation, translation for multiple text styles, back-translation for translation quality evaluation, and grammatical error correction. With this system, we aim to provide a complete translation experience for machine translation users. Our design goals are high translation accuracy, real-time translation, flexibility, and measurable translation quality. Compared with the free commercial translation systems commonly used, our translation assistance system regards the machine translation application as a more complete and fully-featured tool for users. By incorporating additional features and giving the user better control over their experience, we improve translation efficiency and performance. Additionally, our assistant system combines machine translation, grammatical error correction, and interactive edits, and uses a crowdsourcing mode to collect more data for further training to improve both the machine translation and grammatical error correction models. A short video demonstrating our system is available at https://www.youtube.com/watch?v=ZGCo7KtRKd8.

2020

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High-order Semantic Role Labeling
Zuchao Li | Hai Zhao | Rui Wang | Kevin Parnow
Findings of the Association for Computational Linguistics: EMNLP 2020

Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time. High-order features and structure learning were very common in modeling such correlations before the neural network era. In this paper, we introduce a high-order graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new state-of-the-art results.