Zobrazeno 1 - 10 of 17 pro vyhledávání: '"Shuangzhi Wu"'
1
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation
Autor: Jian Yang, Yuwei Yin, Shuming Ma, Dongdong Zhang, Shuangzhi Wu, Hongcheng Guo, Zhoujun Li, Furu Wei
Publikováno v: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages c
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2d7f9b108804f966d2f0c3589e87f75
https://doi.org/10.24963/ijcai.2022/618
Zobrazit plný text záznamu
2
Learning Confidence for Transformer-based Neural Machine Translation
Autor: Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li
Publikováno v: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success. A well-calibrated confidence estimate enables accurate failure prediction and proper risk measurement when given noisy samples and out
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::624c751f40799c2fbc42be26ac7829c2
https://doi.org/10.18653/v1/2022.acl-long.167
Zobrazit plný text záznamu
3
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
Autor: Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li
Publikováno v: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) has achieved remarkable progress with the development of deep neural net
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0ac720aeb03abc6b32bab89b2330d06
https://doi.org/10.18653/v1/2022.naacl-main.375
Zobrazit plný text záznamu
6
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
Autor: Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li
Publikováno v: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75fcda31e19149fad54805269ba09b52
https://doi.org/10.18653/v1/2021.emnlp-main.14
Zobrazit plný text záznamu
7
Enhanced Few-Shot Learning with Multiple-Pattern-Exploiting Training
Autor: Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li
Publikováno v: Natural Language Processing and Chinese Computing ISBN: 9783030884826
NLPCC (2)
The NLPCC 2021 Few-shot Learning for Chinese Language Understanding Evaluation (FewCLUE) shared task seeks for the best solution to few-shot learning tasks with pre-trained language models. This paper presents Tencent Cloud Xiaowei’s approach to th
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::62cfa5776c124cc59fef4df75fdee301
https://doi.org/10.1007/978-3-030-88483-3_31
Zobrazit plný text záznamu
8
Emotion Classification by Jointly Learning to Lexiconize and Classify
Autor: Zhaopeng Tu, Qing Wang, Shuangzhi Wu, Jun Xie, Deyu Zhou, Mu Li
Publikováno v: COLING
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to joint
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::98a81ce6322a2c890a491e6ab10be9b3
https://doi.org/10.18653/v1/2020.coling-main.288
Zobrazit plný text záznamu
9
Dependency-to-Dependency Neural Machine Translation
Autor: Zhirui Zhang, Shuangzhi Wu, Nan Yang, Dongdong Zhang, Ming Zhou, Mu Li
Publikováno v: IEEE/ACM Transactions on Audio, Speech, and Language Processing. 26:2132-2141
Recent research has proven that syntactic knowledge is effective to improve the performance of neural machine translation (NMT). Most previous work focuses on leveraging either source or target syntax in the recurrent neural network (RNN) based encod
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::650868e822ac488522042df8dee59e5a
https://doi.org/10.1109/taslp.2018.2855968
Zobrazit plný text záznamu
10
Effective Soft-Adaptation for Neural Machine Translation
Autor: Shuangzhi Wu, Dongdong Zhang, Ming Zhou
Publikováno v: Natural Language Processing and Chinese Computing ISBN: 9783030322359
NLPCC (2)
Domain mismatch between training data and test data often degrades translation quality. It is necessary to make domain adaptation for machine translation tasks. In this paper, we propose a novel method to tackle Neural Machine Translation (NMT) domai
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7d227aab7ab6672bf937dbb9e56c3739
https://doi.org/10.1007/978-3-030-32236-6_22
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje