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Found 152 papers, 68 papers with code
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task
This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks.
On the Relationship between Neural Machine Translation and Word Alignment
We find that although NMT models are difficult to capture word alignment for CFT words but these words do not sacrifice translation quality significantly, which provides an explanation why NMT is more successful for translation yet worse for word alignment compared to statistical machine translation.
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation
In this work, we provide an appealing alternative for NAT – monolingual KD, which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data.
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing
In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation
In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation.
Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task
This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian.
Tencent Translation System for the WMT21 News Translation Task
Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.
Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task
This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German<->English, English<->German, Chinese<->English and English<->Chinese.
Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task
This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German.
Fine-grained Entity Typing without Knowledge Base
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing
Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).
DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models
Recent advancements in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks.
Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability
Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of natural language processing tasks when leveraging in-context learning.
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation
Existing work addresses this task through a classification model based on a neural network that maps the hidden vector of the input context into its corresponding label (i. e., the candidate target word is treated as a label).
Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning
Iterative preference learning, though yielding superior performances, requires online annotated preference labels.
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction
However, the performance of LLMs on standard knowledge and reasoning benchmarks tends to suffer from deterioration at the latter stage of the SFT process, echoing the phenomenon of alignment tax.
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text
To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text.
CORM: Cache Optimization with Recent Message for Large Language Model Inference
Our validation shows that CORM reduces the inference memory usage of KV cache by up to 70\% with negligible performance degradation across six tasks in LongBench.
Retrieval is Accurate Generation
To address this, we propose to initialize the training oracles using linguistic heuristics and, more importantly, bootstrap the oracles through iterative self-reinforcement.
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
In this work, we investigate the potential of employing the QE model as the reward model to predict human preferences for feedback training.
Benchmarking LLMs via Uncertainty Quantification
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
Knowledge Verification to Nip Hallucination in the Bud
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination.
Knowledge Fusion of Large Language Models
In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM.
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search.
Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models
We present Inferflow, an efficient and highly configurable inference engine for large language models (LLMs).
Alleviating Hallucinations of Large Language Models through Induced Hallucinations
Experimental results on both discrimination-based and generation-based hallucination evaluation benchmarks, such as TruthfulQA and \textsc{FActScore}, demonstrate that our proposed ICD methods can effectively enhance the factuality of LLMs across various model sizes and families.
Reasons to Reject? Aligning Language Models with Judgments
CUT (LLaMA2-chat-13b) can also align LLMs in an iterative fashion using up-to-date model-specific judgments, improving performance from 81. 09 to 91. 68 points on AlpacaEval.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning
Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasoning on various graphs.
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation.
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
Most existing prompting methods suffer from the issues of generalizability and consistency, as they often rely on instance-specific solutions that may not be applicable to other instances and lack task-level consistency across the selected few-shot examples.
Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
To foster progress in this domain, we hold a new shared task at WMT 2023, the first edition of the Discourse-Level Literary Translation.
Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
Ideally, an advanced agent should possess the ability to accurately describe a given word using an aggressive description while concurrently maximizing confusion in the conservative description, enhancing its participation in the game.
Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation.
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks.
RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation
In this paper, we introduce RobustGEC, a benchmark designed to evaluate the context robustness of GEC systems.
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines
This work presents a new task of Text Expansion (TE), which aims to insert fine-grained modifiers into proper locations of the plain text to concretize or vivify human writings.
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.
Ranked #228 on
Visual Question Answering
on MM-Vet
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping
In particular, compared to the best-performing baseline, the LLM trained using our generated dataset exhibits a 10\% relative improvement in performance on AlpacaEval, despite utilizing only 1/5 of its training data.
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers.
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental yet challenging aspect of natural language processing (NLP).
On the Cultural Gap in Text-to-Image Generation
Accordingly, we propose a novel multi-modal metric that considers object-text alignment to filter the fine-tuning data in the target culture, which is used to fine-tune a T2I model to improve cross-cultural generation.
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge.
Explicit Syntactic Guidance for Neural Text Generation
Most existing text generation models follow the sequence-to-sequence paradigm.
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text has not been fully studied.
Rethinking Translation Memory Augmented Neural Machine Translation
The finding demonstrates that TM-augmented NMT is good at the ability of fitting data (i. e., lower bias) but is more sensitive to the fluctuations in the training data (i. e., higher variance), which provides an explanation to a recently reported contradictory phenomenon on the same translation task: TM-augmented NMT substantially advances vanilla NMT under the high-resource scenario whereas it fails under the low-resource scenario.
Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model
Sentence embedding is one of the most fundamental tasks in Natural Language Processing and plays an important role in various tasks.
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
Improved Visual Story Generation with Adaptive Context Modeling
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation.
Enhancing Grammatical Error Correction Systems with Explanations
Grammatical error correction systems improve written communication by detecting and correcting language mistakes.
A Frustratingly Simple Decoding Method for Neural Text Generation
We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation.
MAGE: Machine-generated Text Detection in the Wild
In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources.
A Survey on Zero Pronoun Translation
Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e. g. Chinese, Hungarian, and Hindi), but should be recalled in non-pro-drop languages (e. g. English).
Exploring Human-Like Translation Strategy with Large Language Models
Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process which might take preparatory steps to ensure high-quality translation.
Document-Level Machine Translation with Large Language Models
Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks.
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback
Therefore, we propose ParroT, a framework to enhance and regulate the translation abilities during chat based on open-source LLMs (e. g., LLaMA), human-written translation and feedback data.
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study
Evaluating the quality of generated text is a challenging task in NLP, due to the inherent complexity and diversity of text.
Is ChatGPT A Good Keyphrase Generator? A Preliminary Study
Based on our findings, we conclude that ChatGPT has great potential for keyphrase generation.
Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine
By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e. g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages.
Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning
The spread of rumors along with breaking events seriously hinders the truth in the era of social media.
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure
With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications.
Tencent's Multilingual Machine Translation System for WMT22 Large-Scale African Languages
This paper describes Tencent's multilingual machine translation systems for the WMT22 shared task on Large-Scale Machine Translation Evaluation for African Languages.
Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task
Finally, our unconstrained system achieves BLEU scores of 17. 0 and 30. 4 for English to/from Livonian.
Effidit: Your AI Writing Assistant
In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME).
One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code
Moreover, our model supports self-supervised pretraining with the same sparsely activated way, resulting in better initialized parameters for different modalities.
Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors
We achieve this by introducing a special token \texttt{[null]}, the prediction of which stands for the non-existence of a word.
SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approach
We present SkillNet-NLG, a sparsely activated approach that handles many natural language generation tasks with one model.
A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve its performance.
Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics
Current practices in metric evaluation focus on one single dataset, e. g., Newstest dataset in each year's WMT Metrics Shared Task.
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation
In this paper, we present a substantial step in better understanding the SOTA sequence-to-sequence (Seq2Seq) pretraining for neural machine translation~(NMT).
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation
By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) style gap (i. e., translated vs. natural text style) that leads to poor generalization capability; (2) content gap that induces the model to produce hallucination content biased towards the target language.
MarkBERT: Marking Word Boundaries Improves Chinese BERT
We present a Chinese BERT model dubbed MarkBERT that uses word information in this work.
Chinese Named Entity Recognition
named-entity-recognition
+7
Efficient Sub-structured Knowledge Distillation
Specifically, we transfer the knowledge from a teacher model to its student model by locally matching their predictions on all sub-structures, instead of the whole output space.
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding
The key feature of our approach is that it is sparsely activated guided by predefined skills.
Exploring and Adapting Chinese GPT to Pinyin Input Method
We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin.
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words
(2) how to predict a word via cloze test without knowing the number of wordpieces in advance?
On the Evaluation Metrics for Paraphrase Generation
In this paper we revisit automatic metrics for paraphrase evaluation and obtain two findings that disobey conventional wisdom: (1) Reference-free metrics achieve better performance than their reference-based counterparts.
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing
It has been shown that natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack, which utilizes a `backdoor trigger' paradigm to mislead the models.
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).
Rethinking Negative Sampling for Handling Missing Entity Annotations
Negative sampling is highly effective in handling missing annotations for named entity recognition (NER).
TexSmart: A System for Enhanced Natural Language Understanding
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
On the Copying Behaviors of Pre-Training for Neural Machine Translation
In response to this problem, we propose a simple and effective method named copying penalty to control the copying behaviors in decoding.
On the Language Coverage Bias for Neural Machine Translation
Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice.
Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction
We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (\textbf{TtT}) non-autoregressive sequence prediction to address the deep issues hidden in CGEC.
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data.
GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation
In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic.
REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation
We first clarify an assumption on reference-based metrics that, if more high-quality references are added into the reference set, the reliability of the metric will increase.
TranSmart: A Practical Interactive Machine Translation System
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Dialogue Response Selection with Hierarchical Curriculum Learning
As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.
Ranked #3 on
Conversational Response Selection
on RRS
Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation
The multiplayer online battle arena (MOBA) games have become increasingly popular in recent years.
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
Segmenting Natural Language Sentences via Lexical Unit Analysis
In this work, we present Lexical Unit Analysis (LUA), a framework for general sequence segmentation tasks.
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases.
On the Branching Bias of Syntax Extracted from Pre-trained Language Models
Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing.
On the Sub-Layer Functionalities of Transformer Decoder
There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role.
Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting game results (win predictions).
Evaluating Explanation Methods for Neural Machine Translation
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.
On the Inference Calibration of Neural Machine Translation
Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output.
Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models
In this paper, we bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table -- an interpretable table of bilingual lexicons.
SongNet: Rigid Formats Controlled Text Generation
(3) Although they are restricted to some formats, the sentence integrity must be guaranteed.
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero).
Ranked #11 on
Conversational Response Selection
on E-commerce
Understanding Learning Dynamics for Neural Machine Translation
Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.
Detecting and Understanding Generalization Barriers for Neural Machine Translation
Generalization to unseen instances is our eternal pursuit for all data-driven models.
CASE: Context-Aware Semantic Expansion
In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner.
Neuron Interaction Based Representation Composition for Neural Machine Translation
Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e. g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors.
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each domain in a unified model.
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}.
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data.
Multi-Granularity Self-Attention for Neural Machine Translation
Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information.
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons
Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.
Towards Understanding Neural Machine Translation with Word Importance
Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory.
One Model to Learn Both: Zero Pronoun Prediction and Translation
In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models.
Fine-Grained Sentence Functions for Short-Text Conversation
Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query.
On the Word Alignment from Neural Machine Translation
Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models.
Topic-Aware Neural Keyphrase Generation for Social Media Language
Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
Exploiting Sentential Context for Neural Machine Translation
In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).
Understanding and Improving Hidden Representations for Neural Machine Translation
Multilayer architectures are currently the gold standard for large-scale neural machine translation.
Microblog Hashtag Generation via Encoding Conversation Contexts
Automatic hashtag annotation plays an important role in content understanding for microblog posts.
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement
With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation.
Neural Machine Translation with Adequacy-Oriented Learning
Although Neural Machine Translation (NMT) models have advanced state-of-the-art performance in machine translation, they face problems like the inadequate translation.
Ranked #34 on
Machine Translation
on WMT2014 English-German
Generating Multiple Diverse Responses for Short-Text Conversation
In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously.
Exploiting Deep Representations for Neural Machine Translation
Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures.
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording.
QuaSE: Sequence Editing under Quantifiable Guidance
For example, an input sequence could be a word sequence, such as review sentence and advertisement text.
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method
We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus.
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory
Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways.
Language Style Transfer from Sentences with Arbitrary Unknown Styles
Then, by recombining the content with the target style, we decode a sentence aligned in the target domain.
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction.
Target Foresight Based Attention for Neural Machine Translation
In neural machine translation, an attention model is used to identify the aligned source words for a target word (target foresight word) in order to select translation context, but it does not make use of any information of this target foresight word at all.
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems
This paper presents a large-scale corpus for non-task-oriented dialogue response selection, which contains over 27K distinct prompts more than 82K responses collected from social media.
hyperdoc2vec: Distributed Representations of Hypertext Documents
Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life.
Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user engagement.
Generative Stock Question Answering
We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing action recommendations to stocks upon user's requests.
QuaSE: Accurate Text Style Transfer under Quantifiable Guidance
For example, an input sequence could be a word sequence, such as review sentence and advertisement text.
Translating Pro-Drop Languages with Reconstruction Models
Next, the annotated source sentence is reconstructed from hidden representations in the NMT model.
Learning to Remember Translation History with a Continuous Cache
Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information.
Deep Neural Solver for Math Word Problems
This paper presents a deep neural solver to automatically solve math word problems.
Ranked #4 on
Math Word Problem Solving
on ALG514
Learning Fine-Grained Expressions to Solve Math Word Problems
This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.
