所有论文

CS-Bench:A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

Computer Science (CS) stands as a testament to the intricacies of human intelligence, profoundly advancing the development of …
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CS-Bench:A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

BootTOD:Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses

Pre-trained language models have been successful in many scenarios. However, their usefulness in task-oriented dialogues is limited due …
PDF DOI COLING 2024
BootTOD:Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses

DivTOD:Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations

Language models pre-trained on general text have achieved impressive results in diverse fields. Yet, the distinct linguistic …
PDF DOI NAACL 2024
DivTOD:Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations

Faceptor:A Generalist Model for Face Perception

With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a …
PDF 代码 DOI ECCV 2024
Faceptor:A Generalist Model for Face Perception

PreAct:Predicting Future in ReAct Enhances Agent's Planning Ability

Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and …
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PreAct:Predicting Future in ReAct Enhances Agent's Planning Ability

Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models

In the deployment of large language models (LLMs), accurate confidence estimation is critical for assessing the credibility of model …
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Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models

DolphCoder:Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning

Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning …
PDF DOI ACL 2024
DolphCoder:Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning

Knowledge Editing on Black-box Large Language Models

Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific …
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Knowledge Editing on Black-box Large Language Models

Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking

Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time …
PDF 代码 DOI EMNLP 2023
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking

Revisit input perturbation problems for llms: A unified robustness evaluation framework for noisy slot filling task

We utilize a multi-level data augmentation method (character, word, and sentence levels) to construct a candidate data pool, and …
PDF 代码 DOI NLPCC 2023
Revisit input perturbation problems for llms: A unified robustness evaluation framework for noisy slot filling task

Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT

The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to …
PDF 代码 DOI EMNLP 2023
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT

DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task

Recently, prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks. However, in practical …
PDF 代码 DOI EMNLP 2023
DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task

Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition

In a practical dialogue system, users may input out-of-domain (OOD) queries. The Generalized Intent Discovery (GID) task aims to …
PDF 代码 DOI EMNLP 2023
Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition

Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and …
PDF 代码 DOI CIKM 2023
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA

APP：Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection

Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system. Previous OOD detection …
PDF DOI EMNLP 2023
APP：Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection

A multi-task semantic decomposition framework with task-specific pre-training for few-shot ner

The objective of few-shot named entity recognition is to identify named entities with limited labeled instances. Previous works have …
PDF 代码 DOI CIKM 2023
A multi-task semantic decomposition framework with task-specific pre-training for few-shot ner

Value type-the bridge to a better DST model

Value type of the slots can provide lots of useful information for DST tasks. However, it has been ignored in most previous works. In …
PDF DOI ACL 2023
Value type-the bridge to a better DST model

Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation

Existing controllable dialogue generation work focuses on the single-attribute control and lacks generalization capability to …
PDF DOI ACL 2023
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation

Revisit Out-Of-Vocabulary Problem For Slot Filling: A Unified Contrastive Framework With Multi-Level Data Augmentations

In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced …
PDF DOI ICASSP 2023
Revisit Out-Of-Vocabulary Problem For Slot Filling: A Unified Contrastive Framework With Multi-Level Data Augmentations

Generative zero-shot prompt learning for cross-domain slot filling with inverse prompting

Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Existing …
PDF 代码 DOI ACL 2023
Generative zero-shot prompt learning for cross-domain slot filling with inverse prompting

FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue

Pre-trained language models based on general text enable huge success in the NLP scenario. But the intrinsical difference of linguistic …
PDF 代码 DOI ACL 2023
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue

Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery

Generalized intent discovery aims to extend a closed-set in-domain intent classifier to an open-world intent set including in-domain …
PDF 代码 DOI ACL 2023
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery

A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Named Entity Recognition

Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing …
PDF DOI ICASSP 2023
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Named Entity Recognition

Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery

Discovering out-of-domain (OOD) intent is important for developing new skills in task-oriented dialogue systems. The key challenges lie …
EMNLP 2022
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. …
EMNLP 2022
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking

Collecting dialogue data with domain-slot-value labels for dialogue state tracking (DST) could be a costly process. In this paper, we …
EMNLP 2022
Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking

Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) faces two specific challenges: 1) How to capture useful entity-related visual information; …
EMNLP 2022 Findings
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition

Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. Traditional …
EMNLP 2022 workshop
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning

Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems

Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals. …
PDF 代码 EMNLP2022 workshop (SereTOD)
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling

Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data. …
PDF COLING 2022
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling

Generalized Intent Discovery: Learning from Open World Dialogue System

Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent …
PDF 代码 COLING 2022
Generalized Intent Discovery: Learning from Open World Dialogue System

Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation

Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside …
PDF 代码 COLING 2022
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation

ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement

Traditional dialogue summarization models rely on a large-scale manually-labeled corpus, lacking generalization ability to new domains, …
SIGIR 2022
ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement

Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of …
NAACL 2022
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain …
NAACL 2022
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning

Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system. The key challenge is …
ACL 2022
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning

A Robust Contrastive Alignment Method For Multi-Domain Text Classification

Multi-domain text classification can automatically classify texts in various scenarios. Due to the diversity of human languages, texts …
ICASSP 2022

Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models

The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the …
PDF 代码 EMNLP 2021
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models

Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization

Neural abstractive summarization systems have gained significant progress in recent years. However, excessive abstractiveness …
PDF 代码 EMNLP 2021
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization

Give the Truth:Incorporate Semantic Slot into Abstractive Dialogue Summarization

Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the …
PDF EMNLP 2021
Give the Truth:Incorporate Semantic Slot into Abstractive Dialogue Summarization

Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion:Re-explore Zero-Shot Learning for Slot Filling

Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused …
PDF 代码 EMNLP 2021
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion:Re-explore Zero-Shot Learning for Slot Filling

A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization

Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on …
PDF 代码 EMNLP 2021
A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization

Scheduled Dialog Policy Learning:An Automatic Curriculum Learning Framework for Task-oriented Dialog System

PDF DOI ACL 2021
Scheduled Dialog Policy Learning:An Automatic Curriculum Learning Framework for Task-oriented Dialog System

Novel Slot Detection:A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System

Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. In the practical application, …
PDF 代码 ACL 2021
Novel Slot Detection:A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System

Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task oriented dialog system. A key challenge of …
PDF 代码 ACL 2021
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning

ConSERT:A Contrastive Framework for Self-Supervised Sentence Representation Transfer

Learning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based …
PDF 代码 ACL 2021
ConSERT:A Contrastive Framework for Self-Supervised Sentence Representation Transfer

Hierarchical Speaker-aware Sequence-to-sequence Model for Dialogue Summarization

PDF DOI ICASSP 2021
Hierarchical Speaker-aware Sequence-to-sequence Model for Dialogue Summarization

Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack

Representation learning is widely used in NLP for a vast range of tasks. However, representations derived from text corpora often …
PDF 代码 DOI NAACL 2021
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack

Adversarial Self-Supervised Learning for Out-of-Domain Detection

Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system. Previous unsupervised OOD detection …
PDF 代码 DOI NAACL 2021
Adversarial Self-Supervised Learning for Out-of-Domain Detection

Adversarial Generative Distance-Based Classifier for Robust Out-of-Domain Detection

Detecting out-of-domain (OOD) intents is critical in a task-oriented dialog system. Existing methods rely heavily on extensive manually …
PDF DOI ICASSP 2021
Adversarial Generative Distance-Based Classifier for Robust Out-of-Domain Detection

Utilizing Graph Neural Networks to Improving Dialogue-based Relation Extraction

Relation extraction has been an active research interest in the field of Natural Language Processing (NLP). The past works primarily …
PDF DOI Neurocomputing
Utilizing Graph Neural Networks to Improving Dialogue-based Relation Extraction

From context-aware:to knowledge-aware Boosting OOV tokens recognition in slot tagging with background knowledge

Neural-based context-aware models for slot tagging tasks in language understanding have achieved state-of-the-art performance, …
PDF DOI Neurocomputing
From context-aware:to knowledge-aware Boosting OOV tokens recognition in slot tagging with background knowledge

Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words

Recently, people have been beginning paying more attention to the abstractive dialogue summarization task. Since the information flows …
PDF DOI COLING 2020
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words

Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack

Zero-shot slot filling has widely arisen to cope with data scarcity in target domains. However, previous approaches often ignore …
PDF DOI COLING 2020
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack

A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space

Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system. Different from most existing methods that …
PDF 代码 DOI COLING 2020
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space

Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots

Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot …
PDF 代码 DOI EMNLP 2020
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots

CGTR:Convolution Graph Topology Representation for Document Ranking

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better text …
PDF 代码 DOI CIKM 2020
CGTR:Convolution Graph Topology Representation for Document Ranking

Learning Label-Relational Output Structure for Adaptive Sequence Labeling

Sequence labeling is a fundamental task of natural language understanding. Recent neural models for sequence labeling task achieve …
PDF DOI IJCNN 2020
Learning Label-Relational Output Structure for Adaptive Sequence Labeling

Adversarial Cross-Lingual Transfer Learning for Slot Tagging of Low-Resource Languages

Slot tagging is a key component in a task-oriented dialogue system. Conversational agents need to understand human input by training on …
PDF DOI IJCNN 2020
Adversarial Cross-Lingual Transfer Learning for Slot Tagging of Low-Resource Languages

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge

Neural-based context-aware models for slot tagging have achieved state-of-the-art performance. However, the presence of …
PDF DOI ACL 2020
Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge

Generative Adversarial Zero-Shot Relation Learning for Knowledge Grapths

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, …
PDF DOI AAAI 2020
Generative Adversarial Zero-Shot Relation Learning for Knowledge Grapths

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not …
PDF 代码 DOI ACL 2018
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction

Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works …
PDF DOI ACL 2018
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction