Search Results for author: Wanying Xie

Found 10 papers, 5 papers with code

GX at SemEval-2021 Task 2: BERT with Lemma Information for MCL-WiC Task

1 code implementation SEMEVAL 2021 Wanying Xie

This paper presents the GX system for the Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) task.

LEMMA Machine Translation +3

Importance-based Neuron Allocation for Multilingual Neural Machine Translation

1 code implementation ACL 2021 Wanying Xie, Yang Feng, Shuhao Gu, Dong Yu

Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages.

General Knowledge Machine Translation +1

Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation

1 code implementation NAACL 2021 Shuhao Gu, Yang Feng, Wanying Xie

Domain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both the general-domain and in-domain.

Domain Adaptation Knowledge Distillation +2

Token-level Adaptive Training for Neural Machine Translation

1 code implementation EMNLP 2020 Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie zhou, Dong Yu

The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different frequencies and tends to generate more high-frequency tokens and less low-frequency tokens compared with the golden token distribution.

Machine Translation NMT +1

Modeling Fluency and Faithfulness for Diverse Neural Machine Translation

1 code implementation30 Nov 2019 Yang Feng, Wanying Xie, Shuhao Gu, Chenze Shao, Wen Zhang, Zhengxin Yang, Dong Yu

Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution.

Machine Translation Translation

BLCU\_NLP at SemEval-2019 Task 7: An Inference Chain-based GPT Model for Rumour Evaluation

no code implementations SEMEVAL 2019 Ruoyao Yang, Wanying Xie, Chunhua Liu, Dong Yu

Researchers have been paying increasing attention to rumour evaluation due to the rapid spread of unsubstantiated rumours on social media platforms, including SemEval 2019 task 7.

Rumour Detection

BLCU\_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking

no code implementations SEMEVAL 2019 Wanying Xie, Mengxi Que, Ruoyao Yang, Chunhua Liu, Dong Yu

For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task.

Community Question Answering Fact Checking

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