Search Results for author:
Found 145 papers, 45 papers with code
Learning Structural Information for Syntax-Controlled Paraphrase Generation
To model the alignment relation between words and nodes, we propose an attention regularization objective, which makes the decoder accurately select corresponding syntax nodes to guide the generation of words. Experiments show that SI-SCP achieves state-of-the-art performances in terms of semantic and syntactic quality on two popular benchmark datasets. Additionally, we propose a Syntactic Template Retriever (STR) to retrieve compatible syntactic structures.
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks
As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset.
Adaptive Differential Privacy for Language Model Training
Although differential privacy (DP) can protect language models from leaking privacy, its indiscriminative protection on all data points reduces its practical utility.
A Test Suite for Evaluating Discourse Phenomena in Document-level Neural Machine Translation
The need to evaluate the ability of context-aware neural machine translation (NMT) models in dealing with specific discourse phenomena arises in document-level NMT.
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data
Particularly, we design a two-stage learning method to effectively train the model using non-parallel data.
Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification
Difficult samples of the minority class in imbalanced text classification are usually hard to be classified as they are embedded into an overlapping semantic region with the majority class.
CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation
In this paper, we propose a document-level neural machine translation framework, CoDoNMT, which models cohesion devices from two perspectives: Cohesion Device Masking (CoDM) and Cohesion Attention Focusing (CoAF).
Language Branch Gated Multilingual Neural Machine Translation
Knowledge transfer across languages is crucial for multilingual neural machine translation.
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation
Paraphrasing, i. e., restating the same meaning in different ways, is an important data augmentation approach for natural language processing (NLP).
CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLP
Is there a principle to guide transfer learning across tasks in natural language processing (NLP)?
KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base
In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios.
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context
Experiment results show that the Chinese pretrained language model PanGu-\alpha is 45 points behind human in terms of top-1 word prediction accuracy, indicating that Chinese WPLC is a challenging dataset.
AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation
In end-to-end speech translation, acoustic representations learned by the encoder are usually fixed and static, from the perspective of the decoder, which is not desirable for dealing with the cross-modal and cross-lingual challenge in speech translation.
Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation
Recent studies on end-to-end speech translation(ST) have facilitated the exploration of multilingual end-to-end ST and end-to-end simultaneous ST.
N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning
To bridge this gap, we conduct a thorough analysis of ICL for cross-lingual sentiment classification.
Cross-Lingual Sentiment Classification
In-Context Learning
+3
Evaluating Discourse Cohesion in Pre-trained Language Models
Large pre-trained neural models have achieved remarkable success in natural language process (NLP), inspiring a growing body of research analyzing their ability from different aspects.
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation
Our experiments and analyses demonstrate that neural machine translation performs poorly on commonsense reasoning of the three ambiguity types in terms of both reasoning accuracy (60. 1%) and reasoning consistency (31%).
ProBench: Benchmarking Large Language Models in Competitive Programming
To bridge the gap for high-level code reasoning assessment, we propose ProBench to benchmark LLMs in competitive programming, drawing inspiration from the International Collegiate Programming Contest.
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts
Moreover, we find that Finedeep achieves optimal results when balancing depth and width, specifically by adjusting the number of expert sub-layers and the number of experts per sub-layer.
Evaluating and Improving Graph to Text Generation with Large Language Models
Although we explored the optimal prompting strategies and proposed a novel and effective diversity-difficulty-based few-shot sample selection method, we found that the improvements from tuning-free approaches were incremental, as LLMs struggle with planning on complex graphs, particularly those with a larger number of triplets.
DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search
Further fine-tuning with the identified scaling factors effectively extends the context window of LLMs.
Large Language Model Safety: A Holistic Survey
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation.
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
Our empirical studies, including instruction tuning experiments with models such as Pythia and LLaMA, demonstrate the effectiveness of the proposed framework.
GhostRNN: Reducing State Redundancy in RNN with Cheap Operations
Recurrent neural network (RNNs) that are capable of modeling long-distance dependencies are widely used in various speech tasks, eg., keyword spotting (KWS) and speech enhancement (SE).
Multilingual Large Language Models: A Systematic Survey
An important focus of this survey is on the evaluation of MLLMs.
Self-Pluralising Culture Alignment for Large Language Models
In this paper, we propose CultureSPA, a Self-Pluralising Culture Alignment framework that allows LLMs to simultaneously align to pluralistic cultures.
LANDeRMT: Detecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation
For the detected neurons, we further propose a conditional awareness-based routing mechanism to dynamically adjust language-general and language-specific capacity within LLMs, guided by translation signals.
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning
In this framework, we present a reinforcement-based approach to dynamically estimating source task weights that measure the contribution of the corresponding tasks to the target task in the meta-transfer learning.
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models
These help us curate CMoralEval that encompasses both explicit moral scenarios (14, 964 instances) and moral dilemma scenarios (15, 424 instances), each with instances from different data sources.
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
To mitigate this challenge, we present FuxiTranyu, an open-source multilingual LLM, which is designed to satisfy the need of the research community for balanced and high-performing multilingual capabilities.
Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons
With these identified task-specific neurons, we delve into two common problems in multi-task learning and continuous learning: Generalization and Catastrophic Forgetting.
Controllable Conversations: Planning-Based Dialogue Agent with Large Language Models
Conversational agents powered by Large Language Models (LLMs) show superior performance in various tasks.
Automated Progressive Red Teaming
To address this gap, we propose Automated Progressive Red Teaming (APRT) as an effectively learnable framework.
IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons
To mitigate such knowledge conflicts, we propose a novel framework, IRCAN (Identifying and Reweighting Context-Aware Neurons) to capitalize on neurons that are crucial in processing contextual cues.
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
The advent of large language models (LLMs) has predominantly catered to high-resource languages, leaving a disparity in performance for low-resource languages.
Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning
Subsequently, sentences most similar to the summary are retrieved from the datastore as demonstrations, which effectively guide LLMs in generating cohesive and coherent translations.
CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for Large Language Models
Each question is accompanied with 4 answer choices that state opinions or behavioral tendencies corresponding to the question.
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents
However, after manual evaluation of the validation set, we find that by correcting gold label errors and improving dataset annotation schema, GPT-4 with our prompts can achieve (1) 89. 6%-96. 8% accuracy in DST, and (2) more than 99% correct response generation across different languages.
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
The core of our approach is the observation that a pre-trained language model can confidently predict multiple contiguous tokens, forming the basis for a \textit{lexical unit}, in which these contiguous tokens could be decoded in parallel.
ConTrans: Weak-to-Strong Alignment Engineering via Concept Transplantation
From the perspective of representation engineering, ConTrans refines concept vectors in value alignment from a source LLM (usually a weak yet aligned LLM).
LFED: A Literary Fiction Evaluation Dataset for Large Language Models
The rapid evolution of large language models (LLMs) has ushered in the need for comprehensive assessments of their performance across various dimensions.
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation
Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages.
LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Current datasets collect questions from Chinese examinations across different subjects and educational levels to address this issue.
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models
We conduct extensive experiments on a wide range of LLMs on FineMath and find that there is still considerable room for improvements in terms of mathematical reasoning capability of Chinese LLMs.
Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?
Prior research has revealed that certain abstract concepts are linearly represented as directions in the representation space of LLMs, predominantly centered around English.
Do Large Language Models Mirror Cognitive Language Processing?
In this paper, we employ Representational Similarity Analysis (RSA) to measure the alignment between 23 mainstream LLMs and fMRI signals of the brain to evaluate how effectively LLMs simulate cognitive language processing.
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
Our experimental results indicate that LLMs with 4-bit quantization can retain performance comparable to their non-quantized counterparts, and perplexity can serve as a proxy metric for quantized LLMs on most benchmarks.
RoleEval: A Bilingual Role Evaluation Benchmark for Large Language Models
We expect that RoleEval would highlight the significance of assessing role knowledge for large language models across various languages and cultural settings.
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models
With these pre-defined domains and slots, we collect 76, 787 commonsense knowledge annotations from 19, 700 dialogues through crowdsourcing.
AI-driven emergence of frequency information non-uniform distribution via THz metasurface spectrum prediction
Recently, artificial intelligence has been extensively deployed across various scientific disciplines, optimizing and guiding the progression of experiments through the integration of abundant datasets, whilst continuously probing the vast theoretical space encapsulated within the data.
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
In pursuit of this goal, various benchmarks have been constructed to evaluate the instruction-following capacity of these models.
Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?
Multilingual pretrained language models serve as repositories of multilingual factual knowledge.
Towards a Deep Understanding of Multilingual End-to-End Speech Translation
In this paper, we employ Singular Value Canonical Correlation Analysis (SVCCA) to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages.
DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models
The ability of data memorization and regurgitation in pretrained language models, revealed in previous studies, brings the risk of data leakage.
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?
In particular, we use character-, word-, and multi-level noises to attack the specific translation direction of the multilingual neural machine translation model and evaluate the robustness of other translation directions.
Evaluating Large Language Models: A Comprehensive Survey
We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs.
Large Language Model Alignment: A Survey
We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.
Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy
To mitigate potential risks associated with language models, recent AI detection research proposes incorporating watermarks into machine-generated text through random vocabulary restrictions and utilizing this information for detection.
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models
In this work, we present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models, covering stereotypes and societal biases in 14 social dimensions related to Chinese culture and values.
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Comprehensively evaluating the capability of large language models in multiple tasks is of great importance.
Inverse Reinforcement Learning for Text Summarization
We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors.
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks
It broadcasts the global model in the server to each client and produces pseudo data for clients so that knowledge from the global model can be explored to enhance few-shot learning of each client model.
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework
Knowledge distillation (KD) has been widely used for model compression and knowledge transfer.
NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering
Hybrid tabular-textual question answering (QA) requires reasoning from heterogeneous information, and the types of reasoning are mainly divided into numerical reasoning and span extraction.
Informative Language Representation Learning for Massively Multilingual Neural Machine Translation
Experiment results on two datasets for massively multilingual neural machine translation demonstrate that language-aware multi-head attention benefits both supervised and zero-shot translation and significantly alleviates the off-target translation issue.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Unsupervised and Few-shot Parsing from Pretrained Language Models
We therefore extend the unsupervised models to few-shot parsing models (FPOA, FPIO) that use a few annotated trees to learn better linear projection matrices for parsing.
Efficient Cluster-Based k-Nearest-Neighbor Machine Translation
k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT).
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension
To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement.
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network
The proposed framework only requires cognitive processing signals recorded under natural reading as inputs, and can be used to detect a wide range of linguistic features with a single cognitive dataset.
Secoco: Self-Correcting Encoding for Neural Machine Translation
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting predictors.
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter
For autoregressive NMT models that generate target words from left to right, we observe that adversarial attack on the source language is more effective than on the target language, and that attacking front positions of target sentences or positions of source sentences aligned to the front positions of corresponding target sentences is more effective than attacking other positions.
Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation
This has to be computed n times for a sequence of length n. The linear transformations involved in the LSTM gate and state computations are the major cost factors in this.
Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation
In this paper, we propose to efficiently increase the capacity for multilingual NMT by increasing the cardinality.
TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models
Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error.
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals
Most previous studies integrate cognitive language processing signals (e. g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i. e., textual vs. cognitive) and noise in cognitive features.
Autocorrect in the Process of Translation --- Multi-task Learning Improves Dialogue Machine Translation
Automatic translation of dialogue texts is a much needed demand in many real life scenarios.
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Automatic translation of dialogue texts is a much needed demand in many real life scenarios.
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning
In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Integrating Pre-trained Model into Rule-based Dialogue Management
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility.
Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding
In this paper, we propose an object-level visual context modeling framework (OVC) to efficiently capture and explore visual information for multimodal machine translation.
Modeling Homophone Noise for Robust Neural Machine Translation
In this paper, we propose a robust neural machine translation (NMT) framework.
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling
Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding.
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning
We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training.
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling
In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations.
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation.
Learning Hard Retrieval Decoder Attention for Transformers
The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily.
Rewiring the Transformer with Depth-Wise LSTMs
Stacking non-linear layers allows deep neural networks to model complicated functions, and including residual connections in Transformer layers is beneficial for convergence and performance.
Learning Source Phrase Representations for Neural Machine Translation
Considering that modeling phrases instead of words has significantly improved the Statistical Machine Translation (SMT) approach through the use of larger translation blocks ("phrases") and its reordering ability, modeling NMT at phrase level is an intuitive proposal to help the model capture long-distance relationships.
Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change
We propose to automatically and dynamically determine batch sizes by accumulating gradients of mini-batches and performing an optimization step at just the time when the direction of gradients starts to fluctuate.
Modeling Long Context for Task-Oriented Dialogue State Generation
Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation.
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers
Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches.
Shallow Discourse Annotation for Chinese TED Talks
Text corpora annotated with language-related properties are an important resource for the development of Language Technology.
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue
The training of task-oriented dialogue systems is often confronted with the lack of annotated data.
Merging External Bilingual Pairs into Neural Machine Translation
As neural machine translation (NMT) is not easily amenable to explicit correction of errors, incorporating pre-specified translations into NMT is widely regarded as a non-trivial challenge.
Learning to Reuse Translations: Guiding Neural Machine Translation with Examples
In this paper, we study the problem of enabling neural machine translation (NMT) to reuse previous translations from similar examples in target prediction.
Lipschitz Constrained Parameter Initialization for Deep Transformers
In this paper, we first empirically demonstrate that a simple modification made in the official implementation, which changes the computation order of residual connection and layer normalization, can significantly ease the optimization of deep Transformers.
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation
In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT).
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels
We analyze BiPaR in depth and find that BiPaR offers good diversification in prefixes of questions, answer types and relationships between questions and passages.
Generating Highly Relevant Questions
The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer.
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue
Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues.
Towards Linear Time Neural Machine Translation with Capsule Networks
In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}.
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English- German and English-French language pairs in terms of both translation quality and speed.
Encoding Gated Translation Memory into Neural Machine Translation
Translation memories (TM) facilitate human translators to reuse existing repetitive translation fragments.
Neural Machine Translation with Decoding History Enhanced Attention
Neural machine translation with source-side attention have achieved remarkable performance.
Sentence Weighting for Neural Machine Translation Domain Adaptation
In this paper, we propose a new sentence weighting method for the domain adaptation of neural machine translation.
Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model
Neural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information.
Accelerating Neural Transformer via an Average Attention Network
To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer.
Ranked #60 on
Machine Translation
on WMT2014 English-German
Variational Recurrent Neural Machine Translation
Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches
Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text.
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
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.
Translating Phrases in Neural Machine Translation
Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does.
Modeling Source Syntax for Neural Machine Translation
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.
A GRU-Gated Attention Model for Neural Machine Translation
In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder.
Improving Translation Selection with Supersenses
Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT).
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences
Parallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing.
Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling
After that, we fully incorporate information of different linguistic units into a bilinear semantic similarity model.
Improving Statistical Machine Translation with Selectional Preferences
In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the context of machine translation.
Learning Event Expressions via Bilingual Structure Projection
Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts.
Neural Machine Translation Advised by Statistical Machine Translation
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.
Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation
Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.
Cseq2seq: Cyclic Sequence-to-Sequence Learning
The vanilla sequence-to-sequence learning (seq2seq) reads and encodes a source sequence into a fixed-length vector only once, suffering from its insufficiency in modeling structural correspondence between the source and target sequence.
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations.
Variational Neural Machine Translation
Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.
Variational Neural Discourse Relation Recognizer
In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.
Neural Discourse Relation Recognition with Semantic Memory
Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion.
