Search Results for author: Weihua Luo

Found 46 papers, 17 papers with code

Towards Enhancing Faithfulness for Neural Machine Translation

no code implementations EMNLP 2020 Rongxiang Weng, Heng Yu, Xiangpeng Wei, Weihua Luo

Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences.

Decoder Machine Translation +3

RoBLEURT Submission for WMT2021 Metrics Task

no code implementations WMT (EMNLP) 2021 Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao

After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.

Denoising

Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees

no code implementations11 Jun 2024 Sijia Chen, Yibo Wang, Yi-Feng Wu, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Lijun Zhang

In this study, we propose an inference trajectory optimization framework based on the preference data extracted from decision trees to address this limitation.

M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark

no code implementations8 Jun 2024 Wei Song, Yadong Li, Jianhua Xu, Guowei Wu, Lingfeng Ming, Kexin Yi, Weihua Luo, Houyi Li, Yi Du, Fangda Guo, Kaicheng Yu

As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intelligence.

Attribute

Wings: Learning Multimodal LLMs without Text-only Forgetting

1 code implementation5 Jun 2024 Yi-Kai Zhang, Shiyin Lu, Yang Li, Yanqing Ma, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye

Initially, image and text inputs are aligned with visual learners operating alongside the main attention, balancing focus on visual elements.

Question Answering Visual Question Answering

Parrot: Multilingual Visual Instruction Tuning

1 code implementation4 Jun 2024 Hai-Long Sun, Da-Wei Zhou, Yang Li, Shiyin Lu, Chao Yi, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye

In this paper, we introduce Parrot, a novel method that utilizes textual guidance to drive visual token alignment at the language level.

Ovis: Structural Embedding Alignment for Multimodal Large Language Model

1 code implementation31 May 2024 Shiyin Lu, Yang Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Han-Jia Ye

However, the misalignment between two embedding strategies in MLLMs -- the structural textual embeddings based on an embedding look-up table and the continuous embeddings generated directly by the vision encoder -- makes challenges for a more seamless fusion of visual and textual information.

Multimodal Large Language Model Visual Question Answering (VQA)

RoBLEURT Submission for the WMT2021 Metrics Task

no code implementations28 Apr 2022 Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao

After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.

Denoising

Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation

2 code implementations ACL 2022 Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Weihua Luo, Jun Xie, Rong Jin

Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.

Data Augmentation Machine Translation +3

QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task

no code implementations30 Dec 2021 Jiayi Wang, Ke Wang, Boxing Chen, Yu Zhao, Weihua Luo, Yuqi Zhang

Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years.

Machine Translation Sentence +1

KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation

1 code implementation15 Dec 2021 Xin Liu, Dayiheng Liu, Baosong Yang, Haibo Zhang, Junwei Ding, Wenqing Yao, Weihua Luo, Haiying Zhang, Jinsong Su

Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.

Retrieval Sentence

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

1 code implementation Findings (EMNLP) 2021 Xin Zheng, Zhirui Zhang, ShuJian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen

Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.

Machine Translation NMT +3

Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables

1 code implementation Findings (EMNLP) 2021 Weizhi Wang, Zhirui Zhang, Yichao Du, Boxing Chen, Jun Xie, Weihua Luo

However, it usually suffers from capturing spurious correlations between the output language and language invariant semantics due to the maximum likelihood training objective, leading to poor transfer performance on zero-shot translation.

Denoising Machine Translation +2

Task-Oriented Dialogue System as Natural Language Generation

1 code implementation31 Aug 2021 Weizhi Wang, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen, Weihua Luo

In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing.

Text Generation Transfer Learning

Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction

1 code implementation Findings (ACL) 2021 Jinpeng Zhang, Baijun Ji, Nini Xiao, Xiangyu Duan, Min Zhang, Yangbin Shi, Weihua Luo

Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces.

Bilingual Lexicon Induction Word Embeddings

Context-Interactive Pre-Training for Document Machine Translation

no code implementations NAACL 2021 Pengcheng Yang, Pei Zhang, Boxing Chen, Jun Xie, Weihua Luo

Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information.

Machine Translation Sentence +1

G-Transformer for Document-level Machine Translation

1 code implementation ACL 2021 Guangsheng Bao, Yue Zhang, Zhiyang Teng, Boxing Chen, Weihua Luo

However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail.

Document Level Machine Translation Inductive Bias +3

Adaptive Nearest Neighbor Machine Translation

3 code implementations ACL 2021 Xin Zheng, Zhirui Zhang, Junliang Guo, ShuJian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.

Machine Translation NMT +2

Towards Variable-Length Textual Adversarial Attacks

no code implementations16 Apr 2021 Junliang Guo, Zhirui Zhang, Linlin Zhang, Linli Xu, Boxing Chen, Enhong Chen, Weihua Luo

In this way, our approach is able to more comprehensively find adversarial examples around the decision boundary and effectively conduct adversarial attacks.

Machine Translation Translation

Translation Memory Guided Neural Machine Translation

no code implementations1 Jan 2021 Shaohui Kuang, Heng Yu, Weihua Luo, Qiang Wang

Existing ways either employ extra encoder to encode information from TM or concatenate source sentence and TM sentences as encoder's input.

Decoder Language Modelling +5

Exploiting Neural Query Translation into Cross Lingual Information Retrieval

no code implementations26 Oct 2020 Liang Yao, Baosong Yang, Haibo Zhang, Weihua Luo, Boxing Chen

As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.

Cross-Lingual Information Retrieval Data Augmentation +5

Uncertainty-Aware Semantic Augmentation for Neural Machine Translation

no code implementations EMNLP 2020 Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Luxi Xing, Weihua Luo

As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other.

Machine Translation NMT +3

Iterative Domain-Repaired Back-Translation

no code implementations EMNLP 2020 Hao-Ran Wei, Zhirui Zhang, Boxing Chen, Weihua Luo

In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent.

Domain Adaptation NMT +1

On Learning Universal Representations Across Languages

no code implementations ICLR 2021 Xiangpeng Wei, Rongxiang Weng, Yue Hu, Luxi Xing, Heng Yu, Weihua Luo

Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.

Contrastive Learning Cross-Lingual Natural Language Inference +4

Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences

1 code implementation ACL 2020 Xiangyu Duan, Baijun Ji, Hao Jia, Min Tan, Min Zhang, Boxing Chen, Weihua Luo, Yue Zhang

In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.

Machine Translation Translation +1

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

Multiscale Collaborative Deep Models for Neural Machine Translation

1 code implementation ACL 2020 Xiangpeng Wei, Heng Yu, Yue Hu, Yue Zhang, Rongxiang Weng, Weihua Luo

Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train.

Machine Translation NMT +1

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

GRET: Global Representation Enhanced Transformer

no code implementations24 Feb 2020 Rongxiang Weng, Hao-Ran Wei, Shu-Jian Huang, Heng Yu, Lidong Bing, Weihua Luo, Jia-Jun Chen

The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence.

Decoder Machine Translation +4

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

Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation

no code implementations3 Dec 2019 Baijun Ji, Zhirui Zhang, Xiangyu Duan, Min Zhang, Boxing Chen, Weihua Luo

However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side.

Machine Translation NMT +2

Contrastive Attention Mechanism for Abstractive Sentence Summarization

1 code implementation IJCNLP 2019 Xiangyu Duan, Hoongfei Yu, Mingming Yin, Min Zhang, Weihua Luo, Yue Zhang

We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence.

Abstractive Text Summarization Sentence +1

Improving Neural Machine Translation with Pre-trained Representation

no code implementations21 Aug 2019 Rongxiang Weng, Heng Yu, Shu-Jian Huang, Weihua Luo, Jia-Jun Chen

Then, we design a framework for integrating both source and target sentence-level representations into NMT model to improve the translation quality.

Machine Translation NMT +3

Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention

1 code implementation ACL 2019 Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, Weihua Luo

But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system.

Sentence Sentence Summarization +1

Improved English to Russian Translation by Neural Suffix Prediction

no code implementations11 Jan 2018 Kai Song, Yue Zhang, Min Zhang, Weihua Luo

Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.

Machine Translation NMT +1

Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings

no code implementations ACL 2018 Shaohui Kuang, Junhui Li, António Branco, Weihua Luo, Deyi Xiong

In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase.

Machine Translation Sentence +2

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