Search Results for author:
Found 119 papers, 60 papers with code
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation
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.
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation
The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation.
Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering
2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model.
On Large Language Models' Selection Bias in Multi-Choice Questions
Multi-choice questions (MCQs) serve as a common yet important task format in the research of large language models (LLMs).
Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
The experimental results demonstrate significant improvements in translation performance with SWIE based on BLOOMZ-3b, particularly in zero-shot and long text translations due to reduced instruction forgetting risk.
Instruction Position Matters in Sequence Generation with Large Language Models
Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning.
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Moreover, we find that ALPACA can maintain more knowledge and capacity compared with LLAMA during the continual fine-tuning, which implies that general instruction tuning can help mitigate the forgetting phenomenon of LLMs in the further fine-tuning process.
Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluation
To address this issue, we propose to utilize \textit{multiple references} to enhance the consistency between these metrics and human evaluations.
Towards Codable Text Watermarking for Large Language Models
As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs.
TIM: Teaching Large Language Models to Translate with Comparison
Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning.
Soft Language Clustering for Multilingual Model Pre-training
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size.
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning
In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.
D$^2$TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization
Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task.
Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion
It is seldom considered to adapt the decision boundary for new representations and in this paper we propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning (SCCL), In our method, a contrastive loss is used to directly learn representations for different tasks and a limited number of data samples are saved as the classification criterion.
Personality Understanding of Fictional Characters during Book Reading
As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived.
Towards Unifying Multi-Lingual and Cross-Lingual Summarization
In this paper, we aim to unify MLS and CLS into a more general setting, i. e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training
In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC^3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs.
WeLayout: WeChat Layout Analysis System for the ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents
Our approach effectively demonstrates the benefits of combining query-based and anchor-free models for achieving robust layout segmentation in corporate documents.
Investigating Forgetting in Pre-Trained Representations Through Continual Learning
Intuitively, the representation forgetting can influence the general knowledge stored in pre-trained language models (LMs), but the concrete effect is still unclear.
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias
To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions.
Unified Model Learning for Various Neural Machine Translation
Specifically, we propose a ``versatile'' model, i. e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible.
BranchNorm: Robustly Scaling Extremely Deep Transformers
Recently, DeepNorm scales Transformers into extremely deep (i. e., 1000 layers) and reveals the promising potential of deep scaling.
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
In detail, we regard ChatGPT as a human evaluator and give task-specific (e. g., summarization) and aspect-specific (e. g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models.
Zero-Shot Cross-Lingual Summarization via Large Language Models
Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language.
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation
To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues.
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner.
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization
However, less attention has been paid to the visual features from the perspective of the summary, which may limit the model performance, especially in the low- and zero-resource scenarios.
Understanding Translationese in Cross-Lingual Summarization
Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language.
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions.
Findings of the WMT 2022 Shared Task on Translation Suggestion
We report the result of the first edition of the WMT shared task on Translation Suggestion (TS).
BJTU-WeChat's Systems for the WMT22 Chat Translation Task
Our systems achieve 0. 810 and 0. 946 COMET scores.
Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task
This paper introduces WeChat's participation in WMT 2022 shared biomedical translation task on Chinese to English.
CSCD-IME: Correcting Spelling Errors Generated by Pinyin IME
In fact, most of Chinese input is based on pinyin input method, so the study of spelling errors in this process is more practical and valuable.
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension
Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances.
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response.
Towards Robust k-Nearest-Neighbor Machine Translation
Meanwhile we inject two types of perturbations into the retrieved pairs for robust training.
Categorizing Semantic Representations for Neural Machine Translation
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks.
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment
Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing.
Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement
While the post-editing effort can be used to measure the translation quality to some extent, we find it usually conflicts with the human judgement on whether the word is well or poorly translated.
Probing Causes of Hallucinations in Neural Machine Translations
Hallucination, one kind of pathological translations that bothers Neural Machine Translation, has recently drawn much attention.
Scheduled Multi-task Learning for Neural Chat Translation
Neural Chat Translation (NCT) aims to translate conversational text into different languages.
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation.
Ranked #1 on
Causal Emotion Entailment
on RECCON
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation
Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving restriction.
A Survey on Cross-Lingual Summarization
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
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.
A Variational Hierarchical Model for Neural Cross-Lingual Summarization
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).
Towards Robust Online Dialogue Response Generation
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.
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
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).
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation
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.
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance.
Ranked #3 on
Causal Emotion Entailment
on RECCON
MSCTD: A Multimodal Sentiment Chat Translation Dataset
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.
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation
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.
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization
We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
WeTS: A Benchmark for Translation Suggestion
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.
Simulated annealing for optimization of graphs and sequences
The key idea is to integrate powerful neural networks into metaheuristics (e. g., simulated annealing, SA) to restrict the search space in discrete optimization.
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog
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.
Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation
Visual dialogue is a challenging task since it needs to answer a series of coherent questions on the basis of understanding the visual environment.
Competence-based Curriculum Learning for Multilingual Machine Translation
Therefore, we focus on balancing the learning competencies of different languages and propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M.
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
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.
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages.
Scheduled Sampling Based on Decoding Steps for Neural Machine Translation
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.
WeChat Neural Machine Translation Systems for WMT21
This paper introduces WeChat AI's participation in WMT 2021 shared news translation task on English->Chinese, English->Japanese, Japanese->English and English->German.
Modeling Bilingual Conversational Characteristics for Neural Chat Translation
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.
Confidence-Aware Scheduled Sampling for Neural Machine Translation
In this way, the model is exactly exposed to predicted tokens for high-confidence positions and still ground-truth tokens for low-confidence positions.
Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition
In this paper, we tackle the problem by transferring knowledge from three aspects, i. e., domain, language and task, and strengthening connections among them.
Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue
In Reinforcement Learning, it is crucial to represent states and assign rewards based on the action-caused transitions of states.
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space
Current evaluation of an NMT system is usually built upon a heuristic decoding algorithm (e. g., beam search) and an evaluation metric assessing similarity between the translation and golden reference.
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously.
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation
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.
GTM: A Generative Triple-Wise Model for Conversational Question Generation
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.
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
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
Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.
Selective Knowledge Distillation for Neural Machine Translation
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.
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
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.
Prevent the Language Model from being Overconfident in Neural Machine Translation
The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation.
Tree frog-inspired nanopillar arrays for enhancement of adhesion and friction
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
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.
Multi-Unit Transformers for Neural Machine Translation
Transformer models achieve remarkable success in Neural Machine Translation.
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph
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.
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection
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.
Token-level Adaptive Training for Neural Machine Translation
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.
WeChat Neural Machine Translation Systems for WMT20
We participate in the WMT 2020 shared news translation task on Chinese to English.
Dynamic Context-guided Capsule Network for Multimodal Machine Translation
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.
Ranked #3 on
Multimodal Machine Translation
on Multi30K
Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis
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.
A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images.
Dual Past and Future for Neural Machine Translation
Though remarkable successes have been achieved by Neural Machine Translation (NMT) in recent years, it still suffers from the inadequate-translation problem.
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs.
Ranked #6 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Faster Depth-Adaptive Transformers
Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency.
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
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.
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
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 (ABSA)
Sentiment Classification
+2
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis
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 (ABSA)
Multi-Task Learning
+1
Depth-Adaptive Graph Recurrent Network for Text Classification
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.
DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog
Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image.
Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously.
Multi-Zone Unit for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely used to deal with sequence learning problems.
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
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.
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
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.
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.
Ranked #1 on
Slot Filling
on CAIS
Unsupervised Paraphrasing by Simulated Annealing
Unsupervised paraphrase generation is a promising and important research topic in natural language processing.
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations.
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
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.
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations
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
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.
Bridging the Gap between Training and Inference for Neural Machine Translation
Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs).
Ranked #16 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
(using extra training data)
DTMT: A Novel Deep Transition Architecture for Neural Machine Translation
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.
Modeling Localness for Self-Attention Networks
Self-attention networks have proven to be of profound value for its strength of capturing global dependencies.
Ranked #28 on
Machine Translation
on WMT2014 English-German
Neural Machine Translation with Key-Value Memory-Augmented Attention
Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations.
Towards Robust Neural Machine Translation
Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models.
Interactive Attention for Neural Machine Translation
Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence.
Neural Machine Translation with External Phrase Memory
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.
A Deep Memory-based Architecture for Sequence-to-Sequence Learning
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).
Encoding Source Language with Convolutional Neural Network for Machine Translation
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.
