Search Results for author: Jiajun Zhang

Found 110 papers, 33 papers with code

Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding

1 code implementation Findings (ACL) 2022 Shuxian Zou, Shaonan Wang, Jiajun Zhang, Chengqing Zong

More importantly, it demonstrates that it is feasible to decode a certain word within a large vocabulary from its neural brain activity.

Binary Classification Language Modelling

Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering

no code implementations COLING 2022 Qian Wang, Jiajun Zhang

However, the existing clustering methods based on language similarity cannot handle the asymmetric problem in multilingual NMT, i. e., one translation task A can benefit from another translation task B but task B will be harmed by task A.

Clustering Machine Translation +3

Entity-level Cross-modal Learning Improves Multi-modal Machine Translation

no code implementations Findings (EMNLP) 2021 Xin Huang, Jiajun Zhang, Chengqing Zong

Inspired by the findings of (CITATION) that entities are most informative in the image, we propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation.

Machine Translation Representation Learning +1

DEEP-ICL: Definition-Enriched Experts for Language Model In-Context Learning

no code implementations7 Mar 2024 Xingwei Qu, Yiming Liang, Yucheng Wang, Tianyu Zheng, Tommy Yue, Lei Ma, Stephen W. Huang, Jiajun Zhang, Wenhu Chen, Chenghua Lin, Jie Fu, Ge Zhang

It has long been assumed that the sheer number of parameters in large language models (LLMs) drives in-context learning (ICL) capabilities, enabling remarkable performance improvements by leveraging task-specific demonstrations.

Few-Shot Learning In-Context Learning +1

DPPA: Pruning Method for Large Language Model to Model Merging

1 code implementation5 Mar 2024 Yaochen Zhu, Rui Xia, Jiajun Zhang

In this paper, we introduce a dual-stage method termed Dynamic Pruning Partition Amplification (DPPA), devised to tackle the challenge of merging complex fine-tuned models.

Language Modelling Large Language Model

Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media

no code implementations29 Feb 2024 Jiajun Zhang, ZHIXUN LI, Qiang Liu, Shu Wu, Liang Wang

With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society.

Contrastive Learning Fake News Detection

A Survey on Data Selection for LLM Instruction Tuning

1 code implementation4 Feb 2024 Jiahao Wang, Bolin Zhang, Qianlong Du, Jiajun Zhang, Dianhui Chu

Instruction tuning is a vital step of training large language models (LLM), so how to enhance the effect of instruction tuning has received increased attention.

Instruction Following

Ins-HOI: Instance Aware Human-Object Interactions Recovery

1 code implementation15 Dec 2023 Jiajun Zhang, Yuxiang Zhang, Hongwen Zhang, Boyao Zhou, Ruizhi Shao, Zonghai Hu, Yebin Liu

To this end, we introduce an instance-level occupancy field to support simultaneous human/hand and object representation, and a complementary training strategy to handle the lack of instance-level ground truths.

Human-Object Interaction Detection Object +1

MoDS: Model-oriented Data Selection for Instruction Tuning

1 code implementation27 Nov 2023 Qianlong Du, Chengqing Zong, Jiajun Zhang

First, our approach utilizes a quality evaluation model to filter out the high-quality subset from the original instruction dataset, and then designs an algorithm to further select from the high-quality subset a seed instruction dataset with good coverage.

Instruction Following

Align after Pre-train: Improving Multilingual Generative Models with Cross-lingual Alignment

no code implementations14 Nov 2023 Chong Li, Shaonan Wang, Jiajun Zhang, Chengqing Zong

It aligns the internal sentence representations across different languages via multilingual contrastive learning and aligns model outputs by answering prompts in different languages.

Contrastive Learning Sentence

ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model

1 code implementation2 Nov 2023 Jianghao Chen, Pu Jian, Tengxiao Xi, Dongyi Yi, Qianlong Du, Chenglin Ding, Guibo Zhu, Chengqing Zong, Jinqiao Wang, Jiajun Zhang

Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1. 42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds.

Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning

1 code implementation16 Oct 2023 Chong Li, Shaonan Wang, Yunhao Zhang, Jiajun Zhang, Chengqing Zong

We further propose a simple multi-task training method to increase functional specialization and mitigate negative information transfer in multi-task learning.

Multi-Task Learning

Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets

no code implementations10 Oct 2023 Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins

Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features.

Image Retrieval Retrieval

MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023

no code implementations6 Sep 2023 Zhihang Xu, Shaofei Zhang, Xi Wang, Jiajun Zhang, Wenning Wei, Lei He, Sheng Zhao

In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023.

Speech Synthesis

BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing

1 code implementation2 Sep 2023 Chen Wang, Minpeng Liao, Zhongqiang Huang, Jinliang Lu, Junhong Wu, Yuchen Liu, Chengqing Zong, Jiajun Zhang

One is a cascaded approach where outputs (tokens or states) of a separately trained speech recognition system are used as inputs for LLMs, which limits their potential in modeling alignment between speech and text.

speech-recognition Speech Recognition +1

Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models

no code implementations21 Jul 2023 Jiajun Zhang, Georgina Cosma, Sarah Bugby, Axel Finke, Jason Watkins

As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions.

Defect Detection

ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning

no code implementations3 Jul 2023 Yuxiang Zhang, Hongwen Zhang, Liangxiao Hu, Jiajun Zhang, Hongwei Yi, Shengping Zhang, Yebin Liu

For more accurate and physically plausible predictions in world space, our network is designed to learn human motions from a human-centric perspective, which enables the understanding of the same motion captured with different camera trajectories.

3D Human Pose Estimation

BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages

2 code implementations29 May 2023 Wen Yang, Chong Li, Jiajun Zhang, Chengqing Zong

Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages.

Translation

Language Cognition and Language Computation -- Human and Machine Language Understanding

no code implementations12 Jan 2023 Shaonan Wang, Nai Ding, Nan Lin, Jiajun Zhang, Chengqing Zong

Language understanding is a key scientific issue in the fields of cognitive and computer science.

Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation

1 code implementation18 Oct 2022 Chen Wang, Yuchen Liu, Boxing Chen, Jiajun Zhang, Wei Luo, Zhongqiang Huang, Chengqing Zong

Existing zero-shot methods fail to align the two modalities of speech and text into a shared semantic space, resulting in much worse performance compared to the supervised ST methods.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Learning Confidence for Transformer-based Neural Machine Translation

1 code implementation ACL 2022 Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.

Machine Translation NMT +2

Instance-aware Prompt Learning for Language Understanding and Generation

1 code implementation18 Jan 2022 Feihu Jin, Jinliang Lu, Jiajun Zhang, Chengqing Zong

Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token.

Few-Shot Learning

Parameter Differentiation based Multilingual Neural Machine Translation

2 code implementations27 Dec 2021 Qian Wang, Jiajun Zhang

Further analyses reveal that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.

Machine Translation Open-Ended Question Answering +2

Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models

no code implementations NeurIPS Workshop AI4Scien 2021 Shuxian Zou, Shaonan Wang, Jiajun Zhang, Chengqing Zong

However, most of the existing studies have focused on discriminating which one in two stimuli corresponds to the given brain image, which is far from directly generating text from neural activities.

Text Generation

Exploiting Curriculum Learning in Unsupervised Neural Machine Translation

1 code implementation Findings (EMNLP) 2021 Jinliang Lu, Jiajun Zhang

Back-translation (BT) has become one of the de facto components in unsupervised neural machine translation (UNMT), and it explicitly makes UNMT have translation ability.

Machine Translation Translation

CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization

2 code implementations EMNLP 2021 Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).

Attention Calibration for Transformer in Neural Machine Translation

no code implementations ACL 2021 Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions.

Machine Translation Translation

OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation

2 code implementations1 Jul 2021 Jing Liu, Xinxin Zhu, Fei Liu, Longteng Guo, Zijia Zhao, Mingzhen Sun, Weining Wang, Hanqing Lu, Shiyu Zhou, Jiajun Zhang, Jinqiao Wang

In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.

Audio to Text Retrieval Cross-Modal Retrieval +3

Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

1 code implementation ACL 2021 Yangyifan Xu, Yijin Liu, Fandong Meng, Jiajun Zhang, Jinan Xu, Jie zhou

Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training weights to different tokens, in order to alleviate the token imbalance problem.

Machine Translation Translation

Pre-Training on Dynamic Graph Neural Networks

1 code implementation24 Feb 2021 Ke-Jia Chen, Jiajun Zhang, Linpu Jiang, Yunyun Wang, Yuxuan Dai

This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph.

Graph Generation Graph Sampling +1

Multimodal Sentence Summarization via Multimodal Selective Encoding

no code implementations COLING 2020 Haoran Li, Junnan Zhu, Jiajun Zhang, Xiaodong He, Chengqing Zong

Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.

Sentence Sentence Summarization

Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity

no code implementations COLING 2020 Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong

Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized.

Machine Translation Multi-Task Learning +3

Bridging the Modality Gap for Speech-to-Text Translation

no code implementations28 Oct 2020 Yuchen Liu, Junnan Zhu, Jiajun Zhang, Chengqing Zong

End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way.

Speech-to-Text Translation Translation

Improving Autoregressive NMT with Non-Autoregressive Model

no code implementations WS 2020 Long Zhou, Jiajun Zhang, Cheng-qing Zong

In this work, we propose a novel Encoder-NAD-AD framework for NMT, aiming at boosting AT with global information produced by NAT model.

Knowledge Distillation Machine Translation +2

Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization

no code implementations ACL 2020 Junnan Zhu, Yu Zhou, Jiajun Zhang, Cheng-qing Zong

Cross-lingual summarization aims at summarizing a document in one language (e. g., Chinese) into another language (e. g., English).

Translation

Neural Machine Translation: Challenges, Progress and Future

1 code implementation13 Apr 2020 Jiajun Zhang, Cheng-qing Zong

Machine translation (MT) is a technique that leverages computers to translate human languages automatically.

Machine Translation NMT +1

Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

1 code implementation16 Dec 2019 Yuchen Liu, Jiajun Zhang, Hao Xiong, Long Zhou, Zhongjun He, Hua Wu, Haifeng Wang, Cheng-qing Zong

Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Modelling cosmic ray electron physics in cosmological smoothed particle hydrodynamics simulation

no code implementations1 Dec 2019 Dongchao Zheng, Weitian Li, Zhenghao Zhu, Chenxi Shan, Jiajun Zhang, Linfeng Xiao, Xiaoli Lian, Dan Hu

Cosmic ray electron (CRE) acceleration and cooling are important physical processes in astrophysics.

High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics

Chinese Spelling Error Detection Using a Fusion Lattice LSTM

no code implementations25 Nov 2019 Hao Wang, Bing Wang, Jianyong Duan, Jiajun Zhang

Spelling error detection serves as a crucial preprocessing in many natural language processing applications.

Synchronously Generating Two Languages with Interactive Decoding

no code implementations IJCNLP 2019 Yining Wang, Jiajun Zhang, Long Zhou, Yuchen Liu, Cheng-qing Zong

In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively.

Machine Translation NMT +2

NCLS: Neural Cross-Lingual Summarization

1 code implementation IJCNLP 2019 Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Cheng-qing Zong

Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.

Machine Translation Multi-Task Learning +1

Are You for Real? Detecting Identity Fraud via Dialogue Interactions

1 code implementation IJCNLP 2019 Weikang Wang, Jiajun Zhang, Qian Li, Cheng-qing Zong, Zhifei Li

In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules.

Dialogue Management Fraud Detection +1

Understanding Memory Modules on Learning Simple Algorithms

no code implementations1 Jul 2019 Kexin Wang, Yu Zhou, Shaonan Wang, Jiajun Zhang, Cheng-qing Zong

Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms.

Dimensionality Reduction

Sequence Generation: From Both Sides to the Middle

no code implementations23 Jun 2019 Long Zhou, Jiajun Zhang, Cheng-qing Zong, Heng Yu

The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization.

Machine Translation Sentence +2

Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference

no code implementations ACL 2019 He Bai, Yu Zhou, Jiajun Zhang, Cheng-qing Zong

Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations.

Retrieval slot-filling +2

Synchronous Bidirectional Neural Machine Translation

2 code implementations TACL 2019 Long Zhou, Jiajun Zhang, Cheng-qing Zong

In this paper, we introduce a synchronous bidirectional neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and future information at the same time.

Machine Translation NMT +1

End-to-End Speech Translation with Knowledge Distillation

no code implementations17 Apr 2019 Yuchen Liu, Hao Xiong, Zhongjun He, Jiajun Zhang, Hua Wu, Haifeng Wang, Cheng-qing Zong

End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years.

Knowledge Distillation speech-recognition +2

Synchronous Bidirectional Inference for Neural Sequence Generation

1 code implementation24 Feb 2019 Jiajun Zhang, Long Zhou, Yang Zhao, Cheng-qing Zong

In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively.

Abstractive Text Summarization Machine Translation +1

Language-Independent Representor for Neural Machine Translation

no code implementations1 Nov 2018 Long Zhou, Yuchen Liu, Jiajun Zhang, Cheng-qing Zong, Guoping Huang

Current Neural Machine Translation (NMT) employs a language-specific encoder to represent the source sentence and adopts a language-specific decoder to generate target translation.

Machine Translation Multi-Task Learning +3

A Teacher-Student Framework for Maintainable Dialog Manager

no code implementations EMNLP 2018 Weikang Wang, Jiajun Zhang, Han Zhang, Mei-Yuh Hwang, Cheng-qing Zong, Zhifei Li

Specifically, the {``}student{''} is an extended dialog manager based on a new ontology, and the {``}teacher{''} is existing resources used for guiding the learning process of the {``}student{''}.

Reinforcement Learning (RL)

Associative Multichannel Autoencoder for Multimodal Word Representation

1 code implementation EMNLP 2018 Shaonan Wang, Jiajun Zhang, Cheng-qing Zong

In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs.

Three Strategies to Improve One-to-Many Multilingual Translation

no code implementations EMNLP 2018 Yining Wang, Jiajun Zhang, FeiFei Zhai, Jingfang Xu, Cheng-qing Zong

However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models.

Machine Translation Multi-Task Learning +1

Addressing Troublesome Words in Neural Machine Translation

no code implementations EMNLP 2018 Yang Zhao, Jiajun Zhang, Zhongjun He, Cheng-qing Zong, Hua Wu

One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words.

Machine Translation NMT +1

Phrase Table as Recommendation Memory for Neural Machine Translation

no code implementations25 May 2018 Yang Zhao, Yining Wang, Jiajun Zhang, Cheng-qing Zong

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently.

Machine Translation NMT +2

Learning Multimodal Word Representation via Dynamic Fusion Methods

no code implementations2 Jan 2018 Shaonan Wang, Jiajun Zhang, Cheng-qing Zong

Multimodal models have been proven to outperform text-based models on learning semantic word representations.

Learning from Parenthetical Sentences for Term Translation in Machine Translation

no code implementations WS 2017 Guoping Huang, Jiajun Zhang, Yu Zhou, Cheng-qing Zong

Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific machine translation (MT) tasks.

Machine Translation Sentence +1

Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics

no code implementations15 Nov 2017 Shaonan Wang, Jiajun Zhang, Nan Lin, Cheng-qing Zong

Considering that multimodal models are originally motivated by human concept representations, we assume that correlating multimodal representations with brain-based semantics would interpret their inner properties to answer the above questions.

Learning Semantic Representations Natural Language Understanding

Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT

1 code implementation13 Nov 2017 Yining Wang, Long Zhou, Jiajun Zhang, Cheng-qing Zong

Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation.

Machine Translation NMT +1

Towards Neural Machine Translation with Partially Aligned Corpora

no code implementations IJCNLP 2017 Yining Wang, Yang Zhao, Jiajun Zhang, Cheng-qing Zong, Zhengshan Xue

While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs.

Machine Translation NMT +2

Exploiting Word Internal Structures for Generic Chinese Sentence Representation

no code implementations EMNLP 2017 Shaonan Wang, Jiajun Zhang, Cheng-qing Zong

We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures.

Sentence Sentence Similarity

Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video

no code implementations EMNLP 2017 Haoran Li, Junnan Zhu, Cong Ma, Jiajun Zhang, Cheng-qing Zong

In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic.

Automatic Speech Recognition (ASR) Document Summarization +1

Look-ahead Attention for Generation in Neural Machine Translation

no code implementations30 Aug 2017 Long Zhou, Jiajun Zhang, Cheng-qing Zong

The attention model has become a standard component in neural machine translation (NMT) and it guides translation process by selectively focusing on parts of the source sentence when predicting each target word.

Machine Translation NMT +2

Neural System Combination for Machine Translation

no code implementations ACL 2017 Long Zhou, Wenpeng Hu, Jiajun Zhang, Cheng-qing Zong

Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT).

Machine Translation NMT +1

Shortcut Sequence Tagging

no code implementations3 Jan 2017 Huijia Wu, Jiajun Zhang, Cheng-qing Zong

To simply the stacked architecture, we propose a framework called shortcut block, which is a marriage of the gating mechanism and shortcuts, while discarding the self-connected part in LSTM cell.

POS POS Tagging

Different Contexts Lead to Different Word Embeddings

no code implementations COLING 2016 Wenpeng Hu, Jiajun Zhang, Nan Zheng

Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering.

Clustering Information Retrieval +3

Ultra-Light Axion Dark Matter and its impacts on dark halo structure in $N$-body simulation

1 code implementation3 Nov 2016 Jiajun Zhang, Yue-Lin Sming Tsai, Jui-Lin Kuo, Kingman Cheung, Ming-Chung Chu

The existence of the solitonic core reveals the non-linear effect of quantum pressure and impacts the structure formation in the FDM model.

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies High Energy Physics - Phenomenology

Bridging Neural Machine Translation and Bilingual Dictionaries

no code implementations24 Oct 2016 Jiajun Zhang, Cheng-qing Zong

Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs.

Machine Translation NMT +2

An Empirical Exploration of Skip Connections for Sequential Tagging

no code implementations COLING 2016 Huijia Wu, Jiajun Zhang, Cheng-qing Zong

In this paper, we empirically explore the effects of various kinds of skip connections in stacked bidirectional LSTMs for sequential tagging.

CCG Supertagging POS +1

A Dynamic Window Neural Network for CCG Supertagging

no code implementations10 Oct 2016 Huijia Wu, Jiajun Zhang, Cheng-qing Zong

These motivate us to build a supertagger with a dynamic window approach, which can be treated as an attention mechanism on the local contexts.

CCG Supertagging Sentence +1

Learning Sentence Representation with Guidance of Human Attention

no code implementations29 Sep 2016 Shaonan Wang, Jiajun Zhang, Cheng-qing Zong

Recently, much progress has been made in learning general-purpose sentence representations that can be used across domains.

POS Sentence

One Sentence One Model for Neural Machine Translation

no code implementations LREC 2018 Xiao-Qing Li, Jiajun Zhang, Cheng-qing Zong

Neural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network.

Machine Translation NMT +2

Neural Name Translation Improves Neural Machine Translation

no code implementations7 Jul 2016 Xiao-Qing Li, Jiajun Zhang, Cheng-qing Zong

In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol.

Machine Translation NMT +2

A Bilingual Discourse Corpus and Its Applications

no code implementations LREC 2016 Yang Liu, Jiajun Zhang, Cheng-qing Zong, Yating Yang, Xi Zhou

Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation.

Machine Translation Translation

Local Translation Prediction with Global Sentence Representation

no code implementations27 Feb 2015 Jiajun Zhang

With the sentence-level feature representation, we further design a feed-forward neural network to better predict translations using both local and global information.

Machine Translation Sentence +1

Beyond Word-based Language Model in Statistical Machine Translation

no code implementations5 Feb 2015 Jiajun Zhang, Shujie Liu, Mu Li, Ming Zhou, Cheng-qing Zong

Language model is one of the most important modules in statistical machine translation and currently the word-based language model dominants this community.

Language Modelling Machine Translation +1

Unsupervised Tree Induction for Tree-based Translation

no code implementations TACL 2013 Feifei Zhai, Jiajun Zhang, Yu Zhou, Cheng-qing Zong

In current research, most tree-based translation models are built directly from parse trees.

Machine Translation Translation

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