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Found 28 papers, 15 papers with code
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.
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.
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.
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
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Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems.
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference.
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base
Therefore, an automatic approach for learning the semantics of a data source is desirable.
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning
Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection.
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.
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery
For OOD clustering stage, we propose a KCC method to form compact clusters by mining true hard negative samples, which bridges the gap between clustering and representation learning.
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning
In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.
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 the predefined supported intent set.
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 classes.
Unified Knowledge Prompt Pre-training for Customer Service Dialogues
In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or \textbf{A}ll Tasks), for customer service dialogues.
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.
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.
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.
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.
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.
Syntactic Graph Convolutional Network for Spoken Language Understanding
Slot filling and intent detection are two major tasks for spoken language understanding.
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.
DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner.
