Search Results for author: Nguyen Bach

Found 16 papers, 7 papers with code

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

3 code implementations ACL 2021 Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.

Chinese Named Entity Recognition Chunking +3

ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition

1 code implementation NAACL 2022 Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.

Multi-modal Named Entity Recognition named-entity-recognition +1

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

1 code implementation EMNLP 2021 Xinyin Ma, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Weiming Lu

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing.

Entity Linking Entity Retrieval +1

Unsupervised Multi-modal Neural Machine Translation

no code implementations CVPR 2019 Yuanhang Su, Kai Fan, Nguyen Bach, C. -C. Jay Kuo, Fei Huang

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language.

Machine Translation Translation

FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN

no code implementations WS 2020 Ebrahim Ansari, Amittai Axelrod, Nguyen Bach, Ond{\v{r}}ej Bojar, Roldano Cattoni, Fahim Dalvi, Nadir Durrani, Marcello Federico, Christian Federmann, Jiatao Gu, Fei Huang, Kevin Knight, Xutai Ma, Ajay Nagesh, Matteo Negri, Jan Niehues, Juan Pino, Elizabeth Salesky, Xing Shi, Sebastian St{\"u}ker, Marco Turchi, Alex Waibel, er, Changhan Wang

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.

Translation

Multi-View Cross-Lingual Structured Prediction with Minimum Supervision

no code implementations ACL 2021 Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages.

Cross-Lingual Transfer Sentence +2

Risk Minimization for Zero-shot Sequence Labeling

no code implementations ACL 2021 Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels.

Alibaba Speech Translation Systems for IWSLT 2018

no code implementations IWSLT (EMNLP) 2018 Nguyen Bach, Hongjie Chen, Kai Fan, Cheung-Chi Leung, Bo Li, Chongjia Ni, Rong Tong, Pei Zhang, Boxing Chen, Bin Ma, Fei Huang

This work describes the En→De Alibaba speech translation system developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2018.

Sentence Translation

Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning

no code implementations3 Jun 2022 Yujia Xie, Luowei Zhou, Xiyang Dai, Lu Yuan, Nguyen Bach, Ce Liu, Michael Zeng

Thanks to the strong zero-shot capability of foundation models, we start by constructing a rich semantic representation of the image (e. g., image tags, object attributes / locations, captions) as a structured textual prompt, called visual clues, using a vision foundation model.

Image Paragraph Captioning Language Modelling +1

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