Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification
Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. This paper introduces LARA, a Linguistic-Adaptive Retrieval-Augmentation framework to enhance …
ChatbotClassificationIn-Context Learningintent-classification+4Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification
Intent classifiers must be able to distinguish when a user's utterance does not belong to any supported intent to avoid producing incorrect and unrelated system responses. Although out-of-scope (OOS) detection for intent…
intent-classificationIntent ClassificationCan Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semant…
Clusteringintent-classificationIntent ClassificationNegation+1Augmenting Automation: Intent-Based User Instruction Classification with Machine Learning
Electric automation systems offer convenience and efficiency in controlling electrical circuits and devices. Traditionally, these systems rely on predefined commands for control, limiting flexibility and adaptability. In…
intent-classificationIntent ClassificationPrompt Perturbation Consistency Learning for Robust Language Models
Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However, their performance on sequence labeling t…
Data Augmentationintent-classificationIntent ClassificationIntent Classification and Slot Filling+4LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization
Pretrained language models (PLMs) have become remarkably adept at task and language generalization. Nonetheless, they often fail when faced with unseen languages. In this work, we present LinguAlchemy, a regularization m…
intent-classificationIntent ClassificationLanguage ModellingNews Classification+1Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional…
In-Context Learningintent-classificationIntent ClassificationNatural Language Understanding+4PerSHOP -- A Persian dataset for shopping dialogue systems modeling
Nowadays, dialogue systems are used in many fields of industry and research. There are successful instances of these systems, such as Apple Siri, Google Assistant, and IBM Watson. Task-oriented dialogue system is a categ…
intent-classificationIntent ClassificationNatural Language UnderstandingOmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System
Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue management tasks like dialogue state tracking, dialogue gener…
Dialogue GenerationDialogue ManagementDialogue State Trackingintent-classification+6Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled dat…
intent-classificationIntent ClassificationIntent Classification and Slot FillingRelation+2Bengali Intent Classification with Generative Adversarial BERT
Intent classification is a fundamental task in natural language understanding, aiming to categorize user queries or sentences into predefined classes to understand user intent. The most challenging aspect of this particu…
ClassificationGenerative Adversarial Networkintent-classificationIntent Classification+1Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
Incremental Learning (IL) has been a long-standing problem in both vision and Natural Language Processing (NLP) communities. In recent years, as Pre-trained Language Models (PLMs) have achieved remarkable progress in var…
Class Incremental LearningIncremental Learningintent-classificationIntent Classification+5Creating Spoken Dialog Systems in Ultra-Low Resourced Settings
Automatic Speech Recognition (ASR) systems are a crucial technology that is used today to design a wide variety of applications, most notably, smart assistants, such as Alexa. ASR systems are essentially dialogue systems…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Data Augmentationintent-classification+4Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution
In the quest for efficient neural network models for neural data interpretation and user intent classification in brain-computer interfaces (BCIs), learning meaningful sparse representations of the underlying neural subs…
Efficient Neural Networkintent-classificationIntent ClassificationMotor ImageryGeneralized zero-shot audio-to-intent classification
Spoken language understanding systems using audio-only data are gaining popularity, yet their ability to handle unseen intents remains limited. In this study, we propose a generalized zero-shot audio-to-intent classifica…
ClassificationGoal-Oriented Dialogintent-classificationIntent Classification+4Dense Retrieval as Indirect Supervision for Large-space Decision Making
Many discriminative natural language understanding (NLU) tasks have large label spaces. Learning such a process of large-space decision making is particularly challenging due to the lack of training instances per label a…
Decision MakingEntity TypingExtreme Multi-Label Classificationintent-classification+5Privacy-preserving Representation Learning for Speech Understanding
Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained e…
ClassificationEmotion Recognitionintent-classificationIntent Classification+4IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classifi…
Contrastive Learningintent-classificationIntent ClassificationIntent Detection+3TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
The ability to detect intent in dialogue systems has become increasingly important in modern technology. These systems often generate a large amount of unlabeled data, and manually labeling this data requires substantial…
intent-classificationIntent ClassificationInstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP), yet remain under-explored for task-oriented dialogue systems (TODS), especially for end-to-end TODS. We present Instruct…
Dialogue State TrackingInformativenessintent-classificationIntent Classification+2