Search Results for author:
Found 176 papers, 89 papers with code
SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check
Chinese Spelling Check (CSC) is to detect and correct Chinese spelling errors.
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization
In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation.
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction
The meaning of a word in Chinese is different in that a word is a compositional unit consisting of multiple characters.
{\mathcal{P}^2}: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation
Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems.
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning
For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed.
KNN-Contrastive Learning for Out-of-Domain Intent Classification
The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems.
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models
Therefore, we propose SpeechTokenizer, a unified speech tokenizer for speech large language models.
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
Does Correction Remain A Problem For Large Language Models?
As large language models, such as GPT, continue to advance the capabilities of natural language processing (NLP), the question arises: does the problem of correction still persist?
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Recently, there has been growing interest in extending the context length of instruction-following models in order to effectively process single-turn long input (e. g. summarizing a paper) and conversations with more extensive histories.
Secrets of RLHF in Large Language Models Part I: PPO
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations
Discourse analysis is an important task because it models intrinsic semantic structures between sentences in a document.
From Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest.
Full Parameter Fine-tuning for Large Language Models with Limited Resources
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training.
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs.
Do Large Language Models Know What They Don't Know?
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks.
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System
Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately.
Optimizing Non-Autoregressive Transformers with Contrastive Learning
In this paper, we propose to ease the difficulty of modality learning via sampling from the model distribution instead of the data distribution.
Evaluating the Performance of Large Language Models on GAOKAO Benchmark
Large language models have demonstrated remarkable performance across various natural language processing tasks; however, their efficacy in more challenging and domain-specific tasks remains less explored.
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set.
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors
A common practice is to recast the task into a text-to-text format such that generative LLMs of natural language (NL-LLMs) like GPT-3 can be prompted to solve it.
MoT: Pre-thinking and Recalling Enable ChatGPT to Self-Improve with Memory-of-Thoughts
Large Language Models have shown impressive abilities on various tasks.
Unified Demonstration Retriever for In-Context Learning
To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback.
Improving Contrastive Learning of Sentence Embeddings from AI Feedback
Our method utilizes AI feedback from large pre-trained language models (LLMs) to construct sample pairs with fine-grained sample similarity scores to improve contrastive learning.
Origin Tracing and Detecting of LLMs
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system.
Finding Supporting Examples for In-Context Learning
In this paper, we propose to find ``supporting examples'' for in-context learning: Given the training dataset, we need to select one permutation of a few examples, which are informative for the task's in-context learning and lead to superior performance.
Rethinking Label Smoothing on Multi-hop Question Answering
Label smoothing is a regularization technique widely used in supervised learning to improve the generalization of models on various tasks, such as image classification and machine translation.
Mitigating Negative Style Transfer in Hybrid Dialogue System
As the functionality of dialogue systems evolves, hybrid dialogue systems that accomplish user-specific goals and participate in open-topic chitchat with users are attracting growing attention.
Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next
While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge.
DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models
We present DiffusionBERT, a new generative masked language model based on discrete diffusion models.
Word-Level Representation From Bytes For Language Modeling
Based on this observation, we rethink the existing character-aware method that takes character-level inputs but makes word-level sequence modeling and prediction.
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation.
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
To adapt BERT to the CSC task, we propose a token-level self-distillation contrastive learning method.
DORE: Document Ordered Relation Extraction based on Generative Framework
In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models.
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator
Besides, we introduce a sentence-level and a session-level score to measure the sentence fluency and session coherence in the interactive evaluation.
Discovering New Intents Using Latent Variables
In this paper, starting from the intuition that discovering intents could be beneficial to the identification of the known intents, we propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables.
Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts
Through extensive experimental results across various tasks and PTMs, we show that LPT can achieve competitive performance to full model tuning and other PETuning methods under both full-data and few-shot scenarios while possessing faster training speed and lower memory cost.
Soft-Labeled Contrastive Pre-training for Function-level Code Representation
In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation.
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation
Automatic evaluation metrics are crucial to the development of generative systems.
Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning
MP2 is a set of combinable prompts pre-trained on 38 Chinese tasks.
An Open-World Lottery Ticket for Out-of-Domain Intent Classification
Most existing methods of Out-of-Domain (OOD) intent classification, which rely on extensive auxiliary OOD corpora or specific training paradigms, are underdeveloped in the underlying principle that the models should have differentiated confidence in In- and Out-of-domain intent.
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives.
A Unified Generative Framework based on Prompt Learning for Various Information Extraction Tasks
In this paper, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of Information Extraction.
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
In this paper, we propose using Convolutional Neural Network (CNN) to model these spatial relations in the score matrix.
Ranked #3 on
Nested Named Entity Recognition
on ACE 2005
CoNT: Contrastive Neural Text Generation
We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation.
What Dense Graph Do You Need for Self-Attention?
Transformers have made progress in miscellaneous tasks, but suffer from quadratic computational and memory complexities.
BBTv2: Towards a Gradient-Free Future with Large Language Models
By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment.
Dialogue Meaning Representation for Task-Oriented Dialogue Systems
We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue.
Text Adversarial Purification as Defense against Adversarial Attacks
Adversarial purification is a successful defense mechanism against adversarial attacks without requiring knowledge of the form of the incoming attack.
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
+6
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation
Previous works usually adopt heuristic metrics such as the entropy of internal outputs to measure instance difficulty, which suffers from generalization and threshold-tuning.
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction
The meaning of a word in Chinese is different in that a word is a compositional unit consisting of multiple characters.
$\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning
With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters.
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER
Chinese NER is a difficult undertaking due to the ambiguity of Chinese characters and the absence of word boundaries.
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search
For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build code-text pairs.
Towards Collaborative Question Answering: A Preliminary Study
Knowledge and expertise in the real-world can be disjointedly owned.
Black-Box Tuning for Language-Model-as-a-Service
In such a scenario, which we call Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually unavailable.
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer
Meanwhile, we utilize an auxiliary response generation task to enhance the model's ability of handling context information, thereby forcing the model to recognize emotions with similar semantics in diverse contexts.
Ranked #8 on
Emotion Recognition in Conversation
on EmoryNLP
Towards More Effective and Economic Sparsely-Activated Model
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
ELUE is dedicated to depict the Pareto Frontier for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement.
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier
Pre-trained models are widely used in fine-tuning downstream tasks with linear classifiers optimized by the cross-entropy loss, which might face robustness and stability problems.
RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization
Existing summarization systems mostly generate summaries purely relying on the content of the source document.
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT).
Learning to Teach with Student Feedback
This one-way interaction leads to the teacher's inability to perceive the characteristics of the student and its training progress.
Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
\textbf{P}re-\textbf{T}rained \textbf{M}odel\textbf{s} have been widely applied and recently proved vulnerable under backdoor attacks: the released pre-trained weights can be maliciously poisoned with certain triggers.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
A Unified Generative Framework for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms.
Ranked #1 on
Aspect Sentiment Triplet Extraction
on SemEval
Aspect-oriented Opinion Extraction
Aspect Sentiment Triplet Extraction
+1
A Survey of Transformers
X-formers) have been proposed, however, a systematic and comprehensive literature review on these Transformer variants is still missing.
A Unified Generative Framework for Various NER Subtasks
To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework.
Ranked #10 on
Nested Named Entity Recognition
on GENIA
Accelerating BERT Inference for Sequence Labeling via Early-Exit
To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks.
Early Exiting with Ensemble Internal Classifiers
In this paper, we show that a novel objective function for the training of the ensemble internal classifiers can be naturally induced from the perspective of ensemble learning and information theory.
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning
In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level $F_1$ score, edit distance, duplication, and prediction quantities.
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa
In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree.
Enhancing Scientific Papers Summarization with Citation Graph
Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces
The substitutions in the generated adversarial examples are not characters or words but \textit{'pieces'}, which are more natural to Chinese readers.
Finding Sparse Structures for Domain Specific Neural Machine Translation
Neural machine translation often adopts the fine-tuning approach to adapt to specific domains.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation
With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Ranked #1 on
Unsupervised KG-to-Text Generation
on GenWiki (Fine)
Text Information Aggregation with Centrality Attention
A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves aggregation process of combining the representations of all the words, such as pooling or self-attention.
Pre-training with Meta Learning for Chinese Word Segmentation
Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS).
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information
We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs?
Ranked #3 on
Machine Translation
on WMT2014 English-French
(using extra training data)
CoLAKE: Contextualized Language and Knowledge Embedding
With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of these models.
BERT for Monolingual and Cross-Lingual Reverse Dictionary
Reverse dictionary is the task to find the proper target word given the word description.
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search
Ablation study demonstrates the necessity of our search space design and the effectiveness of our search method.
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP
The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in CWS and POS.
AutoTrans: Automating Transformer Design via Reinforced Architecture Search
Though the transformer architectures have shown dominance in many natural language understanding tasks, there are still unsolved issues for the training of transformer models, especially the need for a principled way of warm-up which has shown importance for stable training of a transformer, as well as whether the task at hand prefer to scale the attention product or not.
Improving Image Captioning with Better Use of Caption
Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.
Improving Image Captioning with Better Use of Captions
Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
In natural language, often multiple entities appear in the same text.
TAVAT: Token-Aware Virtual Adversarial Training for Language Understanding
Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete.
Heterogeneous Graph Neural Networks for Extractive Document Summarization
An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships.
FLAT: Chinese NER Using Flat-Lattice Transformer
Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information.
Ranked #5 on
Chinese Named Entity Recognition
on MSRA
Chinese Named Entity Recognition
named-entity-recognition
+2
BERT-ATTACK: Adversarial Attack Against BERT Using BERT
Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods.
Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on
Text Summarization
on BBC XSum
Unified Multi-Criteria Chinese Word Segmentation with BERT
Besides, the pre-trained BERT language model has been also introduced into the MCCWS task in a multi-task learning framework.
Pre-trained Models for Natural Language Processing: A Survey
Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era.
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
Fine-tuning pre-trained language models like BERT has become an effective way in NLP and yields state-of-the-art results on many downstream tasks.
Multi-Scale Self-Attention for Text Classification
In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules.
Joint Parsing and Generation for Abstractive Summarization
If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged.
Ranked #26 on
Text Summarization
on GigaWord
Learning Sparse Sharing Architectures for Multiple Tasks
Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing.
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
The Transformer model is widely successful on many natural language processing tasks.
Ranked #1 on
Machine Translation
on IWSLT2015 Chinese-English
TENER: Adapting Transformer Encoder for Named Entity Recognition
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
Ranked #11 on
Chinese Named Entity Recognition
on Resume NER
A Closer Look at Data Bias in Neural Extractive Summarization Models
In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models.
Exploring Domain Shift in Extractive Text Summarization
Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization.
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context.
Ranked #3 on
Word Sense Disambiguation
on WiC-TSV
DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks
Variants dropout methods have been designed for the fully-connected layer, convolutional layer and recurrent layer in neural networks, and shown to be effective to avoid overfitting.
Searching for Effective Neural Extractive Summarization: What Works and What's Next
The recent years have seen remarkable success in the use of deep neural networks on text summarization.
Ranked #6 on
Extractive Text Summarization
on CNN / Daily Mail
A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder
Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion.
How to Fine-Tune BERT for Text Classification?
Language model pre-training has proven to be useful in learning universal language representations.
Ranked #1 on
Text Classification
on Yahoo! Answers
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
Disentangling the content and style in the latent space is prevalent in unpaired text style transfer.
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing.
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA).
Aspect-Based Sentiment Analysis (ABSA)
General Classification
+3
Star-Transformer
Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data.
Ranked #13 on
Sentiment Analysis
on SST-5 Fine-grained classification
Named Entity Recognition (NER)
Natural Language Inference
+2
VCWE: Visual Character-Enhanced Word Embeddings
Chinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information.
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria.
Multi-task Learning over Graph Structures
We present two architectures for multi-task learning with neural sequence models.
U-Net: Machine Reading Comprehension with Unanswerable Questions
A key subtask is to reliably predict whether the question is unanswerable.
Ranked #12 on
Question Answering
on SQuAD2.0 dev
Convolutional Interaction Network for Natural Language Inference
Attention-based neural models have achieved great success in natural language inference (NLI).
A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing
This paper describes Fudan{'}s submission to CoNLL 2018{'}s shared task Universal Dependency Parsing.
Deformable Stacked Structure for Named Entity Recognition
Neural architecture for named entity recognition has achieved great success in the field of natural language processing.
Neural Arithmetic Expression Calculator
With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models.
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks
Designing shared neural architecture plays an important role in multi-task learning.
Gaussian Word Embedding with a Wasserstein Distance Loss
Therefore, with the aim of representing words in a highly efficient way, we propose to operate a Gaussian word embedding model with a loss function based on the Wasserstein distance.
Top-Down Tree Structured Text Generation
Text generation is a fundamental building block in natural language processing tasks.
Information Aggregation via Dynamic Routing for Sequence Encoding
The dynamic routing policy is dynamically deciding that what and how much information need be transferred from each word to the final encoding of the text sequence.
Ranked #38 on
Sentiment Analysis
on IMDb
Toward Diverse Text Generation with Inverse Reinforcement Learning
Similar to the adversarial models, the reward and policy function in IRL are optimized alternately.
Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks
More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism.
Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method
Generating plausible and fluent sentence with desired properties has long been a challenge.
Meta Multi-Task Learning for Sequence Modeling
Specifically, we use a shared meta-network to capture the meta-knowledge of semantic composition and generate the parameters of the task-specific semantic composition models.
Idiom-Aware Compositional Distributed Semantics
Idioms are peculiar linguistic constructions that impose great challenges for representing the semantics of language, especially in current prevailing end-to-end neural models, which assume that the semantics of a phrase or sentence can be literally composed from its constitutive words.
DAG-based Long Short-Term Memory for Neural Word Segmentation
In this paper, we propose a new neural model to incorporate the word-level information for Chinese word segmentation.
Overview of the NLPCC 2017 Shared Task: Chinese News Headline Categorization
In this paper, we give an overview for the shared task at the CCF Conference on Natural Language Processing \& Chinese Computing (NLPCC 2017): Chinese News Headline Categorization.
Dynamic Compositional Neural Networks over Tree Structure
Tree-structured neural networks have proven to be effective in learning semantic representations by exploiting syntactic information.
Reinforced Mnemonic Reader for Machine Reading Comprehension
In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
Ranked #17 on
Question Answering
on SQuAD1.1 dev
Adversarial Multi-Criteria Learning for Chinese Word Segmentation
Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS).
Adversarial Multi-task Learning for Text Classification
Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features.
Knowledge Graph Representation with Jointly Structural and Textual Encoding
In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities.
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering.
End-to-End Neural Sentence Ordering Using Pointer Network
However, it is nontrivial for pair-wise models to incorporate the contextual sentence information.
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering.
Deep Multi-Task Learning with Shared Memory
Neural network based models have achieved impressive results on various specific tasks.
Learning Word Embeddings from Intrinsic and Extrinsic Views
To prove the effectiveness of our model, we evaluate it on four tasks, including word similarity, reverse dictionaries, Wiki link prediction, and document classification.
Neural Sentence Ordering
Sentence ordering is a general and critical task for natural language generation applications.
Syntax-based Attention Model for Natural Language Inference
Introducing attentional mechanism in neural network is a powerful concept, and has achieved impressive results in many natural language processing tasks.