Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Practical token pruning for foundation models in few-shot conversational virtual assistant systems
In an enterprise Virtual Assistant (VA) system, intent classification is the crucial component that determines how a user input is handled based on what the user wants. The VA system is expected to be a cost-efficient Sa…
ClassificationContrastive Learningintent-classificationIntent Classification+3A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce
Query intent classification is an essential module for customers to find desired products on the e-commerce application quickly. Most existing query intent classification methods rely on the users' click behavior as a su…
intent-classificationIntent ClassificationSemantic SimilaritySemantic Textual SimilarityExploring Description-Augmented Dataless Intent Classification
In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our m…
AvgClassificationintent-classificationIntent ClassificationWhy do you cite? An investigation on citation intents and decision-making classification processes
Identifying the reason for which an author cites another work is essential to understand the nature of scientific contributions and to assess their impact. Citations are one of the pillars of scholarly communication and …
Citation Intent ClassificationDecision Makingintent-classificationIntent ClassificationOne Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning
This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). Training a high-performance QA system…
Continual LearningContrastive Learningintent-classificationIntent Classification+4Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
Large language models (LLM) have achieved remarkable success in natural language generation but lesser focus has been given to their applicability in decision making tasks such as classification. We show that LLMs like L…
ClassificationDecision Makingintent-classificationIntent Classification+2DASB -- Discrete Audio and Speech Benchmark
Discrete audio tokens have recently gained considerable attention for their potential to connect audio and language processing, enabling the creation of modern multimodal large language models. Ideal audio tokens must ef…
BenchmarkingEmotion Recognitionintent-classificationIntent Classification+7An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks
Self-supervised learning models have revolutionized the field of speech processing. However, the process of fine-tuning these models on downstream tasks requires substantial computational resources, particularly when dea…
Automatic Speech RecognitionDecoderEmotion Recognitionintent-classification+7Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model
Recently, multi-task spoken language understanding (SLU) models have emerged, designed to address various speech processing tasks. However, these models often rely on a large number of parameters. Also, they often encoun…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Emotion Recognitionintent-classification+6Self-Supervised Speech Representations are More Phonetic than Semantic
Self-supervised speech models (S3Ms) have become an effective backbone for speech applications. Various analyses suggest that S3Ms encode linguistic properties. In this work, we seek a more fine-grained analysis of the w…
intent-classificationIntent ClassificationSemantic SimilaritySemantic Textual SimilarityImproved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification
Detecting out-of-scope user utterances is essential for task-oriented dialogues and intent classification. Current methodologies face difficulties with the unpredictable distribution of outliers and often rely on assumpt…
ClassificationDenoisingintent-classificationIntent Classification+1DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers
Navigating the complexities of language diversity is a central challenge in developing robust natural language processing systems, especially in specialized domains like banking. The Moroccan Dialect (Darija) serves as t…
intent-classificationIntent ClassificationIntent DetectionLanguage Modeling+3Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
Recently, deep end-to-end learning has been studied for intent classification in Spoken Language Understanding (SLU). However, end-to-end models require a large amount of speech data with intent labels, and highly optimi…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+6Luganda Speech Intent Recognition for IoT Applications
The advent of Internet of Things (IoT) technology has generated massive interest in voice-controlled smart homes. While many voice-controlled smart home systems are designed to understand and support widely spoken langua…
intent-classificationIntent ClassificationIntent RecognitionSpeech Intent ClassificationOmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs
The progression to "Pervasive Augmented Reality" envisions easy access to multimodal information continuously. However, in many everyday scenarios, users are occupied physically, cognitively or socially. This may increas…
FrictionIn-Context Learningintent-classificationIntent ClassificationCourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education
The growing enrollments in computer science courses and increase in class sizes necessitate scalable, automated tutoring solutions to adequately support student learning. While Large Language Models (LLMs) like GPT-4 hav…
intent-classificationIntent ClassificationQuestion AnsweringRetrieval-augmented GenerationSimulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent classification, slot filling, conversat…
Conversational Question AnsweringDialogue State Trackingintent-classificationIntent Classification+7New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fifteen years, models achieving both of th…
intent-classificationIntent ClassificationIntent Classification and Slot Fillingslot-filling+2Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition
We present Conformal Intent Classification and Clarification (CICC), a framework for fast and accurate intent classification for task-oriented dialogue systems. The framework turns heuristic uncertainty scores of any int…
intent-classificationIntent ClassificationIntent RecognitionTask-Oriented Dialogue SystemsILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler
State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications. Large language models on the other hand,…
In-Context Learningintent-classificationIntent Classificationparameter-efficient fine-tuning+3