Search Results for author: Shanbo Cheng

Found 24 papers, 7 papers with code

G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation

1 code implementation21 May 2024 Xingyuan Pan, Luyang Huang, Liyan Kang, Zhicheng Liu, Yu Lu, Shanbo Cheng

Moreover, to enhance the diversity of the training data we maximize the variety of influences they have on the model by clustering on their gradients and resampling.

Diversity Machine Translation +1

MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation

1 code implementation14 Mar 2024 Jiahuan Li, Shanbo Cheng, ShuJian Huang, Jiajun Chen

Large Language Models (LLM) have demonstrated their strong ability in the field of machine translation (MT), yet they suffer from high computational cost and latency.

Knowledge Distillation Machine Translation +1

Speech Translation with Large Language Models: An Industrial Practice

no code implementations21 Dec 2023 Zhichao Huang, Rong Ye, Tom Ko, Qianqian Dong, Shanbo Cheng, Mingxuan Wang, Hang Li

Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM.

Language Modelling Large Language Model +1

Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation

no code implementations25 Sep 2023 Zihan Liu, Zewei Sun, Shanbo Cheng, ShuJian Huang, Mingxuan Wang

Document-level Neural Machine Translation (DocNMT) has been proven crucial for handling discourse phenomena by introducing document-level context information.

Dimensionality Reduction Machine Translation +1

Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions

no code implementations24 May 2023 Jiahuan Li, Hao Zhou, ShuJian Huang, Shanbo Cheng, Jiajun Chen

Secondly, we find that LLMs' ability to carry out translation instructions relies on the understanding of translation instructions and the alignment among different languages.

Language Modelling Translation

BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation

1 code implementation23 May 2023 Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu, Zewei Sun, Shanbo Cheng, Mingxuan Wang, Degen Huang, Jinsong Su

We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.

Contrastive Learning Multimodal Machine Translation +3

Visual Information Matters for ASR Error Correction

no code implementations16 Mar 2023 Vanya Bannihatti Kumar, Shanbo Cheng, Ningxin Peng, Yuchen Zhang

Aiming to improve the Automatic Speech Recognition (ASR) outputs with a post-processing step, ASR error correction (EC) techniques have been widely developed due to their efficiency in using parallel text data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Controlling Styles in Neural Machine Translation with Activation Prompt

1 code implementation17 Dec 2022 Yifan Wang, Zewei Sun, Shanbo Cheng, Weiguo Zheng, Mingxuan Wang

Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is crucial for enhancing user experience.

Machine Translation NMT +1

Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus

1 code implementation24 Jan 2022 Yaoming Zhu, Liwei Wu, Shanbo Cheng, Mingxuan Wang

The punctuation restoration task aims to correctly punctuate the output transcriptions of automatic speech recognition systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Language-aware Interlingua for Multilingual Neural Machine Translation

no code implementations ACL 2020 Changfeng Zhu, Heng Yu, Shanbo Cheng, Weihua Luo

However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained.

Decoder Diversity +4

AR: Auto-Repair the Synthetic Data for Neural Machine Translation

no code implementations5 Apr 2020 Shanbo Cheng, Shaohui Kuang, Rongxiang Weng, Heng Yu, Changfeng Zhu, Weihua Luo

Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translation (BT) or forward translation (FT, or selftraining), into the NMT training process can significantly improve translation quality.

de-en Machine Translation +3

Acquiring Knowledge from Pre-trained Model to Neural Machine Translation

no code implementations4 Dec 2019 Rongxiang Weng, Heng Yu, Shu-Jian Huang, Shanbo Cheng, Weihua Luo

The standard paradigm of exploiting them includes two steps: first, pre-training a model, e. g. BERT, with a large scale unlabeled monolingual data.

General Knowledge Knowledge Distillation +3

Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages

no code implementations20 Oct 2018 Xin Tang, Shanbo Cheng, Loc Do, Zhiyu Min, Feng Ji, Heng Yu, Ji Zhang, Haiqin Chen

Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data.

Machine Translation Semantic Similarity +4

Cannot find the paper you are looking for? You can Submit a new open access paper.