Search Results for author: Fandong Meng

Found 77 papers, 41 papers with code

Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation

no code implementations EMNLP 2020 Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, Jie zhou

Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection.

Dialogue Generation Knowledge Distillation

Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training

1 code implementation24 Apr 2022 Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie zhou

Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.

Transfer Learning

Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation

no code implementations19 Apr 2022 Siyu Lai, Zhen Yang, Fandong Meng, Xue Zhang, Yufeng Chen, Jinan Xu, Jie zhou

Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving restriction.

Machine Translation Translation

A Survey on Cross-Lingual Summarization

no code implementations23 Mar 2022 Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie zhou

Cross-lingual summarization is the task of generating a summary in one language (e. g., English) for the given document(s) in a different language (e. g., Chinese).

Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene

no code implementations16 Mar 2022 Duo Zheng, Fandong Meng, Qingyi Si, Hairun Fan, Zipeng Xu, Jie zhou, Fangxiang Feng, Xiaojie Wang

Visual dialog has witnessed great progress after introducing various vision-oriented goals into the conversation, especially such as GuessWhich and GuessWhat, where the only image is visible by either and both of the questioner and the answerer, respectively.

Visual Dialog

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

1 code implementation ACL 2022 Yunlong Liang, Fandong Meng, Chulun Zhou, Jinan Xu, Yufeng Chen, Jinsong Su, Jie zhou

The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e. g., English) to a summary in another one (e. g., Chinese).

Machine Translation Translation

Towards Robust Online Dialogue Response Generation

no code implementations7 Mar 2022 Leyang Cui, Fandong Meng, Yijin Liu, Jie zhou, Yue Zhang

Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-world practice, especially in multi-turn settings.

Chatbot Re-Ranking +1

Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

1 code implementation ACL 2022 Songming Zhang, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jian Liu, Jie zhou

Token-level adaptive training approaches can alleviate the token imbalance problem and thus improve neural machine translation, through re-weighting the losses of different target tokens based on specific statistical metrics (e. g., token frequency or mutual information).

Language Modelling Machine Translation +1

EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation

no code implementations ACL 2022 Yulin Xu, Zhen Yang, Fandong Meng, JieZhou

Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior performance against the conventional MNMT by constructing multi-way aligned corpus, i. e., aligning bilingual training examples from different language pairs when either their source or target sides are identical.

Machine Translation

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

1 code implementation2 Mar 2022 Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, Jie zhou

Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance.

Causal Emotion Entailment

MSCTD: A Multimodal Sentiment Chat Translation Dataset

1 code implementation ACL 2022 Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie zhou

In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context.

Multimodal Machine Translation Sentiment Analysis +1

Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation

no code implementations ACL 2022 Chulun Zhou, Fandong Meng, Jie zhou, Min Zhang, Hongji Wang, Jinsong Su

Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner.

Knowledge Distillation Language Modelling +2

ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization

1 code implementation11 Feb 2022 Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu, Jie zhou

We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents.

WeTS: A Benchmark for Translation Suggestion

1 code implementation11 Oct 2021 Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li, Jie zhou

To break this limitation, we create a benchmark data set for TS, called \emph{WeTS}, which contains golden corpus annotated by expert translators on four translation directions.

Machine Translation Translation

Simulated annealing for optimization of graphs and sequences

no code implementations1 Oct 2021 Xianggen Liu, Pengyong Li, Fandong Meng, Hao Zhou, Huasong Zhong, Jie zhou, Lili Mou, Sen Song

The key idea is to integrate powerful neural networks into metaheuristics (e. g., simulated annealing, SA) to restrict the search space in discrete optimization.

Paraphrase Generation

Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation

1 code implementation Findings (ACL) 2021 Feilong Chen, Fandong Meng, Xiuyi Chen, Peng Li, Jie zhou

Visual dialogue is a challenging task since it needs to answer a series of coherent questions on the basis of understanding the visual environment.

Dialogue Generation Visual Grounding

GoG: Relation-aware Graph-over-Graph Network for Visual Dialog

no code implementations Findings (ACL) 2021 Feilong Chen, Xiuyi Chen, Fandong Meng, Peng Li, Jie zhou

Specifically, GoG consists of three sequential graphs: 1) H-Graph, which aims to capture coreference relations among dialog history; 2) History-aware Q-Graph, which aims to fully understand the question through capturing dependency relations between words based on coreference resolution on the dialog history; and 3) Question-aware I-Graph, which aims to capture the relations between objects in an image based on fully question representation.

Coreference Resolution Visual Dialog

Competence-based Curriculum Learning for Multilingual Machine Translation

no code implementations Findings (EMNLP) 2021 Mingliang Zhang, Fandong Meng, Yunhai Tong, Jie zhou

Therefore, we focus on balancing the learning competencies of different languages and propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M.

Machine Translation Translation

Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser

1 code implementation Findings (EMNLP) 2021 Duo Zheng, Zipeng Xu, Fandong Meng, Xiaojie Wang, Jiaan Wang, Jie zhou

To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser (AugG) that is strong and is optimized for the VD setting especially.

Visual Dialog

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation

1 code implementation EMNLP 2021 Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

Its core motivation is to simulate the inference scene during training by replacing ground-truth tokens with predicted tokens, thus bridging the gap between training and inference.

Machine Translation Text Summarization +1

WeChat Neural Machine Translation Systems for WMT21

no code implementations WMT (EMNLP) 2021 Xianfeng Zeng, Yijin Liu, Ernan Li, Qiu Ran, Fandong Meng, Peng Li, Jinan Xu, Jie zhou

This paper introduces WeChat AI's participation in WMT 2021 shared news translation task on English->Chinese, English->Japanese, Japanese->English and English->German.

Knowledge Distillation Machine Translation +3

Modeling Bilingual Conversational Characteristics for Neural Chat Translation

1 code implementation ACL 2021 Yunlong Liang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency.

Machine Translation Translation

Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition

1 code implementation Findings (ACL) 2021 Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

In this paper, we tackle the problem by transferring knowledge from three aspects, i. e., domain, language and task, and strengthening connections among them.

named-entity-recognition Named Entity Recognition +2

Confidence-Aware Scheduled Sampling for Neural Machine Translation

1 code implementation Findings (ACL) 2021 Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

In this way, the model is exactly exposed to predicted tokens for high-confidence positions and still ground-truth tokens for low-confidence positions.

Machine Translation Translation

Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue

1 code implementation12 Jul 2021 Zipeng Xu, Fandong Meng, Xiaojie Wang, Duo Zheng, Chenxu Lv, Jie zhou

In Reinforcement Learning, it is crucial to represent states and assign rewards based on the action-caused transitions of states.

reinforcement-learning

Rethinking the Evaluation of Neural Machine Translation

no code implementations29 Jun 2021 Jianhao Yan, Chenming Wu, Fandong Meng, Jie zhou

However, this evaluation framework suffers from high search errors brought by heuristic search algorithms and is limited by its nature of evaluation over one best candidate.

Machine Translation Translation

Sequence-Level Training for Non-Autoregressive Neural Machine Translation

1 code implementation CL (ACL) 2021 Chenze Shao, Yang Feng, Jinchao Zhang, Fandong Meng, Jie zhou

Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously.

Machine Translation Translation

Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation

1 code implementation ACL 2021 Yuanxin Liu, Fandong Meng, Zheng Lin, Weiping Wang, Jie zhou

In this paper, however, we observe that although distilling the teacher's hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled.

Knowledge Distillation

GTM: A Generative Triple-Wise Model for Conversational Question Generation

no code implementations ACL 2021 Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie zhou

Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction.

Question Generation

Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition

no code implementations NAACL 2021 Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie zhou

Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse.

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

1 code implementation ACL 2021 Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie zhou, Degen Huang, Qingqiang Wu, Jinsong Su

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.

Code Generation

Selective Knowledge Distillation for Neural Machine Translation

1 code implementation ACL 2021 Fusheng Wang, Jianhao Yan, Fandong Meng, Jie zhou

As an active research field in NMT, knowledge distillation is widely applied to enhance the model's performance by transferring teacher model's knowledge on each training sample.

Knowledge Distillation Machine Translation +1

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

Prevent the Language Model from being Overconfident in Neural Machine Translation

1 code implementation ACL 2021 Mengqi Miao, Fandong Meng, Yijin Liu, Xiao-Hua Zhou, Jie zhou

The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation.

Language Modelling Machine Translation +1

Tree frog-inspired nanopillar arrays for enhancement of adhesion and friction

no code implementations4 Mar 2021 Zhekun Shi, Di Tan, Quan Liu, Fandong Meng, Bo Zhu, Longjian Xue

Bioinspired structure adhesives have received increasing interest for many applications, such as climbing robots and medical devices.

Soft Condensed Matter

Emotional Conversation Generation with Heterogeneous Graph Neural Network

1 code implementation9 Dec 2020 Yunlong Liang, Fandong Meng, Ying Zhang, Jinan Xu, Yufeng Chen, Jie zhou

Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i. e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback.

A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph

no code implementations Findings of the Association for Computational Linguistics 2020 Lin Qiao, Jianhao Yan, Fandong Meng, Zhendong Yang, Jie zhou

Therefore, we propose a novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational autoencoder (CVAE) framework.

Text Generation

MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection

no code implementations10 Oct 2020 Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie zhou

As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate.

Answer Selection reinforcement-learning

Token-level Adaptive Training for Neural Machine Translation

1 code implementation EMNLP 2020 Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie zhou, Dong Yu

The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different frequencies and tends to generate more high-frequency tokens and less low-frequency tokens compared with the golden token distribution.

Machine Translation Translation

Dynamic Context-guided Capsule Network for Multimodal Machine Translation

1 code implementation4 Sep 2020 Huan Lin, Fandong Meng, Jinsong Su, Yongjing Yin, Zhengyuan Yang, Yubin Ge, Jie zhou, Jiebo Luo

Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities.

Multimodal Machine Translation Natural Language Processing +2

Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis

no code implementations12 Aug 2020 Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, Jie zhou

For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence.

Aspect-Based Sentiment Analysis

Dual Past and Future for Neural Machine Translation

no code implementations15 Jul 2020 Jianhao Yan, Fandong Meng, Jie zhou

Though remarkable successes have been achieved by Neural Machine Translation (NMT) in recent years, it still suffers from the inadequate-translation problem.

Machine Translation Translation

Faster Depth-Adaptive Transformers

no code implementations27 Apr 2020 Yijin Liu, Fandong Meng, Jie zhou, Yufeng Chen, Jinan Xu

Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency.

Sentence Embeddings Text Classification

Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization

no code implementations26 Apr 2020 Zeyang Lei, Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Yujiu Yang, Cheng Niu, Jie zhou

Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard.

Response Generation

An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis

2 code implementations Findings (EMNLP) 2021 Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, Jie zhou

Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner.

Aspect-Based Sentiment Analysis Term Extraction +1

A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis

3 code implementations4 Apr 2020 Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie zhou

The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.

Aspect-Based Sentiment Analysis Multi-Task Learning +1

Depth-Adaptive Graph Recurrent Network for Text Classification

1 code implementation29 Feb 2020 Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

The Sentence-State LSTM (S-LSTM) is a powerful and high efficient graph recurrent network, which views words as nodes and performs layer-wise recurrent steps between them simultaneously.

Classification General Classification +1

DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog

1 code implementation18 Dec 2019 Feilong Chen, Fandong Meng, Jiaming Xu, Peng Li, Bo Xu, Jie zhou

Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image.

Visual Dialog

Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation

1 code implementation21 Nov 2019 Chenze Shao, Jinchao Zhang, Yang Feng, Fandong Meng, Jie zhou

Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously.

Machine Translation Translation

Multi-Zone Unit for Recurrent Neural Networks

no code implementations17 Nov 2019 Fandong Meng, Jinchao Zhang, Yang Liu, Jie zhou

Recurrent neural networks (RNNs) have been widely used to deal with sequence learning problems.

Aspect-Based Sentiment Analysis Language Modelling

Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation

no code implementations5 Nov 2019 Yong Shan, Yang Feng, Jinchao Zhang, Fandong Meng, Wen Zhang

Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produces an unbalanced translation.

Machine Translation Translation

Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

no code implementations21 Oct 2019 Yingxue Zhang, Ping Jian, Fandong Meng, Ruiying Geng, Wei Cheng, Jie zhou

Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations.

Classification Discourse Parsing +2

CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding

1 code implementation IJCNLP 2019 Yijin Liu, Fandong Meng, Jinchao Zhang, Jie zhou, Yufeng Chen, Jinan Xu

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.

Intent Detection Slot Filling +1

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

1 code implementation IJCNLP 2019 Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie zhou

Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation.

Aspect-Based Sentiment Analysis

Incremental Transformer with Deliberation Decoder for Document Grounded Conversations

2 code implementations ACL 2019 Zekang Li, Cheng Niu, Fandong Meng, Yang Feng, Qian Li, Jie zhou

Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document.

Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation

3 code implementations ACL 2019 Chenze Shao, Yang Feng, Jinchao Zhang, Fandong Meng, Xilin Chen, Jie zhou

Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails to exploit the target sequential information.

Machine Translation Translation

Bridging the Gap between Training and Inference for Neural Machine Translation

no code implementations ACL 2019 Wen Zhang, Yang Feng, Fandong Meng, Di You, Qun Liu

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

Machine Translation Translation

DTMT: A Novel Deep Transition Architecture for Neural Machine Translation

1 code implementation19 Dec 2018 Fandong Meng, Jinchao Zhang

In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT.

Machine Translation Translation

Neural Machine Translation with Key-Value Memory-Augmented Attention

no code implementations29 Jun 2018 Fandong Meng, Zhaopeng Tu, Yong Cheng, Haiyang Wu, Junjie Zhai, Yuekui Yang, Di Wang

Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations.

Machine Translation Translation

Towards Robust Neural Machine Translation

no code implementations ACL 2018 Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, Yang Liu

Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models.

Machine Translation Translation

Interactive Attention for Neural Machine Translation

no code implementations COLING 2016 Fandong Meng, Zhengdong Lu, Hang Li, Qun Liu

Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence.

Machine Translation Translation

Neural Machine Translation with External Phrase Memory

no code implementations6 Jun 2016 Yaohua Tang, Fandong Meng, Zhengdong Lu, Hang Li, Philip L. H. Yu

In this paper, we propose phraseNet, a neural machine translator with a phrase memory which stores phrase pairs in symbolic form, mined from corpus or specified by human experts.

Machine Translation Translation

A Deep Memory-based Architecture for Sequence-to-Sequence Learning

no code implementations22 Jun 2015 Fandong Meng, Zhengdong Lu, Zhaopeng Tu, Hang Li, Qun Liu

We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transformations from the representation of the input sequence (e. g., a Chinese sentence) to the final output sequence (e. g., translation to English).

Machine Translation Translation

Encoding Source Language with Convolutional Neural Network for Machine Translation

no code implementations IJCNLP 2015 Fandong Meng, Zhengdong Lu, Mingxuan Wang, Hang Li, Wenbin Jiang, Qun Liu

The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT.

Language Modelling Machine Translation +1

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