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Found 31 papers, 12 papers with code
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 filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.
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 multi-speaker information interaction process.
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 dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding.
Abstractive Dialogue Summarization
Abstractive Text Summarization
+1
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.
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization
Based on the artificial dataset, we train an evaluation model that can not only make accurate and robust factual consistency discrimination but is also capable of making interpretable factual errors tracing by backpropagated gradient distribution on token embeddings.
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 reasoning paths in the knowledge base (KB).
A Robust Contrastive Alignment Method For Multi-Domain Text Classification
In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning.
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 adaptation in summarization generally rely on large-scale pre-trainings.
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
TODSum: Task-Oriented Dialogue Summarization with State Tracking
Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet.
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 extensive research.
Adversarial Self-Supervised Learning for Out-of-Domain Detection
Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system.
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack
In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task.
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.
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.
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer
Learning high-quality sentence representations benefits a wide range of natural language processing tasks.
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge
In this paper, we present a Knowledge Graph Enhanced Dual-Copy network (KGEDC), a novel framework for abstractive dialogue summarization with conversational structure and factual knowledge.
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.
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack
In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling.
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words
A masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue.
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.
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
Finding Salient Context based on Semantic Matching for Relevance Ranking
In this paper, we propose a salient-context based semantic matching method to improve relevance ranking in information retrieval.
Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation.
Ranked #10 on
Text Summarization
on CNN / Daily Mail (Anonymized)
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.
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.
Neural Regularized Domain Adaptation for Chinese Word Segmentation
For Chinese word segmentation, the large-scale annotated corpora mainly focus on newswire and only a handful of annotated data is available in other domains such as patents and literature.
Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text
Word representation models have achieved great success in natural language processing tasks, such as relation classification.
Bitext Name Tagging for Cross-lingual Entity Annotation Projection
Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.
