Search Results for author:
Found 292 papers, 79 papers with code
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
TinyBERT: Distilling BERT for Natural Language Understanding
To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models.
Ranked #1 on
Natural Language Inference
on MultiNLI Dev
DynaBERT: Dynamic BERT with Adaptive Width and Depth
The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive.
GPT-based Generation for Classical Chinese Poetry
We present a simple yet effective method for generating high quality classical Chinese poetry with Generative Pre-trained Language Model (GPT).
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
Masked language model and autoregressive language model are two types of language models.
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices.
HyperText: Endowing FastText with Hyperbolic Geometry
Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym relations in WordNet.
BinaryBERT: Pushing the Limit of BERT Quantization
In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization.
Training Multilingual Pre-trained Language Model with Byte-level Subwords
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints.
JABER and SABER: Junior and Senior Arabic BERt
Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception.
Universal Conditional Masked Language Pre-training for Neural Machine Translation
To the best of our knowledge, this is the first work to pre-train a unified model for fine-tuning on both NMT tasks.
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
We investigate different aspects of responses generated by PanGu-Bot, including response quality, knowledge, and safety.
CASICT Tibetan Word Segmentation System for MLWS2017
We participated in the MLWS 2017 on Tibetan word segmentation task, our system is trained in a unrestricted way, by introducing a baseline system and 76w tibetan segmented sentences of ours.
ERNIE: Enhanced Language Representation with Informative Entities
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
Ranked #1 on
Entity Linking
on FIGER
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.
PERT: A New Solution to Pinyin to Character Conversion Task
Pinyin to Character conversion (P2C) task is the key task of Input Method Engine (IME) in commercial input software for Asian languages, such as Chinese, Japanese, Thai language and so on.
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme
Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies.
Aligning Large Language Models with Human: A Survey
(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
The student network is trained to output representation resembling that of the teacher.
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning.
Ranked #1 on
Satellite Image Classification
on SAT-4
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (One-Shot)
on DuReader
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+19
Data Management For Large Language Models: A Survey
Data plays a fundamental role in the training of Large Language Models (LLMs).
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages
MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval across 18 different languages, which collectively encompass over three billion native speakers around the world.
Multi-channel Reverse Dictionary Model
A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description.
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT
However, this approach of evaluating a language model is undermined by the uncertainty of the amount of knowledge that is learned by the probe itself.
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Lexical constraints take the form of phrases or words that must be present in the output sequence.
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.
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
Translating Pro-Drop Languages with Reconstruction Models
Next, the annotated source sentence is reconstructed from hidden representations in the NMT model.
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.
Modeling Semantic Compositionality with Sememe Knowledge
In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment.
multi-word expression embedding
multi-word expression sememe prediction
Sub-Character Tokenization for Chinese Pretrained Language Models
2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos.
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos.
Ranked #4 on
Video Question Answering
on MVBench
Knowledge Diffusion for Neural Dialogue Generation
Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.
MINER: Multi-Interest Matching Network for News Recommendation
Accurately matching user’s interests and candidate news is the key to news recommendation.
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning
In this work, we propose a simple and effective method to cover a much larger proportion of the attack search space, called Adversarial and Mixup Data Augmentation (AMDA).
Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks
The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e. g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice.
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models
We believe this is a critical bottleneck for realizing human-like cognition in PLMs.
M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models
In this paper, we propose M4LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation.
Visually Guided Generative Text-Layout Pre-training for Document Intelligence
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and reason both document texts and layouts (e. g., locations of texts and table-cells).
Exploring Extreme Parameter Compression for Pre-trained Language Models
A tiny version achieves $96. 7\%$ performance of BERT-base with $ {1}/{48} $ encoder parameters (i. e., less than 2M parameters excluding the embedding layer) and $2. 7 \times$ faster on inference.
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
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.
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
To address this task, we propose a TOD system with hybrid knowledge management, HyKnow.
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs
Incorporating factual knowledge into pre-trained language models (PLM) such as BERT is an emerging trend in recent NLP studies.
Ranked #11 on
Common Sense Reasoning
on ReCoRD
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Social network alignment aims at aligning person identities across social networks.
Exploiting Cross-Sentence Context for Neural Machine Translation
In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies.
NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation
We measure LLM robustness using two metrics: (i) hallucination rate, measuring model tendency to hallucinate an answer, when the answer is not present in passages in the non-relevant subset, and (ii) error rate, measuring model inaccuracy to recognize relevant passages in the relevant subset.
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e. g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora.
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications.
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers
With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better.
Achieving Reliable Human Assessment of Open-Domain Dialogue Systems
Answering the distress call of competitions that have emphasized the urgent need for better evaluation techniques in dialogue, we present the successful development of human evaluation that is highly reliable while still remaining feasible and low cost.
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Inspired by these observations, we propose Fast Prompt Tuning (FPT), which starts by conducting PT using a small-scale partial PLM, and then progressively expands its depth and width until the full-model size.
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications.
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 ( 6 60. 1{\%}) and reasoning consistency (6 31{\%}).
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings
Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
Preparing Lessons for Progressive Training on Language Models
The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes.
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering
Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability.
PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models
LLMs are prompted to generate extensive content in response to these meta-questions.
mCLIP: Multilingual CLIP via Cross-lingual Transfer
Furthermore, to enhance the token- and sentence-level multilingual representation of the MTE, we propose to train it with machine translation and contrastive learning jointly before the TriKD to provide a better initialization.
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions
In this paper, we propose a framework, MoralDial to train and evaluate moral dialogue systems.
NewsDialogues: Towards Proactive News Grounded Conversation
In this paper, we propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news.
A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering
This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e. g., producing wrong solutions or answers).
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Inspired by previous work which identified that feed-forward networks (FFNs) within Transformers are responsible for factual knowledge expressions, we investigate two methods to efficiently improve the factual expression capability {of FFNs} by knowledge enhancement and alignment respectively.
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation
Monolingual evaluation of Machine Translation (MT) aims to simplify human assessment by requiring assessors to compare the meaning of the MT output with a reference translation, opening up the task to a much larger pool of genuinely qualified evaluators.
Accurate Word Alignment Induction from Neural Machine Translation
Shift-Att is an interpretation method that induces alignments from the attention weights of Transformer and does not require parameter update or architecture change.
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Inspired by adversarial training, we minimize this maximal expected loss (MMEL) and obtain a simple and interpretable closed-form solution: more attention should be paid to augmented samples with large loss values (i. e., harder examples).
Dynamic Multi-Branch Layers for On-Device Neural Machine Translation
With the rapid development of artificial intelligence (AI), there is a trend in moving AI applications, such as neural machine translation (NMT), from cloud to mobile devices.
MTRec: Multi-Task Learning over BERT for News Recommendation
With the adoption of large pre-trained models like BERT in news recommendation, the above way to incorporate multi-field information may encounter challenges: the shallow feature encoding to compress the category and entity information is not compatible with the deep BERT encoding.
Understanding Meanings in Multilingual Customer Feedback
Understanding and being able to react to customer feedback is the most fundamental task in providing good customer service.
Information-Propogation-Enhanced Neural Machine Translation by Relation Model
Even though sequence-to-sequence neural machine translation (NMT) model have achieved state-of-art performance in the recent fewer years, but it is widely concerned that the recurrent neural network (RNN) units are very hard to capture the long-distance state information, which means RNN can hardly find the feature with long term dependency as the sequence becomes longer.
Refining Source Representations with Relation Networks for Neural Machine Translation
Although neural machine translation (NMT) with the encoder-decoder framework has achieved great success in recent times, it still suffers from some drawbacks: RNNs tend to forget old information which is often useful and the encoder only operates through words without considering word relationship.
Refining Source Representations with Relation Networks for Neural Machine Translation
Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network structure, and disregarding relationship between source words during encoding step.
SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensing
This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time.
Unsupervised Learning using Pretrained CNN and Associative Memory Bank
In this paper, we present a new architecture and an approach for unsupervised object recognition that addresses the above mentioned problem with fine tuning associated with pretrained CNN-based supervised deep learning approaches while allowing automated feature extraction.
Ranked #1 on
Fine-Grained Image Classification
on Caltech-101
(Accuracy metric)
Few-Shot Image Classification
Fine-Grained Image Classification
+2
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation
Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches.
Deep Neural Machine Translation with Linear Associative Unit
Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex linguistic structures.
Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation.
Ranked #11 on
Multimodal Machine Translation
on Multi30K
Multilingual Multi-modal Embeddings for Natural Language Processing
We propose a novel discriminative model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality.
Incorporating Global Visual Features into Attention-Based Neural Machine Translation
We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder.
Ranked #10 on
Multimodal Machine Translation
on Multi30K
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human.
Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation Metrics
The widely-used automatic evaluation metrics cannot adequately reflect the fluency of the translations.
Interactive Attention for Neural Machine Translation
Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence.
Memory-enhanced Decoder for Neural Machine Translation
We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN.
Automatic Construction of Discourse Corpora for Dialogue Translation
Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts.
A Novel Approach to Dropped Pronoun Translation
Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences.
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).
Syntax-based Deep Matching of Short Texts
Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts.
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.
$gen$CNN: A Convolutional Architecture for Word Sequence Prediction
Different from previous work on neural network-based language modeling and generation (e. g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector.
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters
Classification techniques for images of handwritten characters are susceptible to noise.
Ranked #1 on
Document Image Classification
on n-MNIST
Speeding Up Neural Machine Translation Decoding by Cube Pruning
Although neural machine translation has achieved promising results, it suffers from slow translation speed.
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism
Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations.
Improving the Robustness of Speech Translation
Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation
This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance.
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking
We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features.
Neural Automatic Post-Editing Using Prior Alignment and Reranking
APE translations produced by our system show statistically significant improvements over the first-stage MT, phrase-based APE and the best reported score on the WMT 2016 APE dataset by a previous neural APE system.
Improving Evaluation of Document-level Machine Translation Quality Estimation
Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable.
Context-Aware Graph Segmentation for Graph-Based Translation
In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration.
Incorporating Global Visual Features into Attention-based Neural Machine Translation.
We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder.
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data
In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data.
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language
In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data.
Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking
A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented.
Tailoring Neural Architectures for Translating from Morphologically Rich Languages
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures.
A subtree-based factorization of dependency parsing
For Chinese, the most notable increase is as high as 3. 63 (UAS) when the proposed framework is applied to first-order parsing models.
Fast Gated Neural Domain Adaptation: Language Model as a Case Study
We show that the neural LM perplexity can be reduced by 7. 395 and 12. 011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively.
Topic-Informed Neural Machine Translation
In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance.
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings
PBSMT engines by default provide four probability scores in phrase tables which are considered as the main set of bilingual features.
Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking
We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management.
ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task
We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task.
Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing
We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality.
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation
High fidelity route choice models are required to predict traffic levels with higher accuracy.
Improving Domain Adaptation Translation with Domain Invariant and Specific Information
Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously.
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.
ProphetMT: A Tree-based SMT-driven Controlled Language Authoring/Post-Editing Tool
This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool.
Decomposable Neural Paraphrase Generation
Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level.
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task
This paper describes Huawei{'}s neural machine translation systems for the WMT 2019 biomedical translation shared task.
Dialog State Tracking with Reinforced Data Augmentation
Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data.
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters
We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even in the presence of noise.
Ranked #1 on
Image Classification
on Noisy MNIST (AWGN)
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation
Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observed, remains challenging due to the syntactic structure difference and simultaneity requirements.
Pretrained Language Models for Document-Level Neural Machine Translation
Previous work on document-level NMT usually focuses on limited contexts because of degraded performance on larger contexts.
Zero-Shot Paraphrase Generation with Multilingual Language Models
Leveraging multilingual parallel texts to automatically generate paraphrases has drawn much attention as size of high-quality paraphrase corpus is limited.
Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks
Synthetic and field data were tested to assess the performance of the proposed algorithm (DSPRecon algorithm); the advantages of using our method were evaluated by comparing it with the singular spectrum analysis (SSA) method for irregular data reconstruction and de-aliased Cadzow method for regular data reconstruction.
Integrating Graph Contextualized Knowledge into Pre-trained Language Models
Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information.
Learning to Predict Explainable Plots for Neural Story Generation
While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge.
Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations
Data modeling and reduction for in situ is important.
Context-Aware Design of Cyber-Physical Human Systems (CPHS)
The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models.
A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium
Myocardial infarction (MI) is a scientific term that refers to heart attack.
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation
Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora.
