Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
SNOiC: Soft Labeling and Noisy Mixup based Open Intent Classification Model
This paper presents a Soft Labeling and Noisy Mixup-based open intent classification model (SNOiC). Most of the previous works have used threshold-based methods to identify open intents, which are prone to overfitting an…
intent-classificationIntent ClassificationImproving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis
Training a high performance end-to-end speech (E2E) processing model requires an enormous amount of labeled speech data, especially in the era of data-centric artificial intelligence. However, labeled speech data are usu…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+5Conversational Factor Information Retrieval Model (ConFIRM)
This paper introduces the Conversational Factor Information Retrieval Method (ConFIRM), a novel approach to fine-tuning large language models (LLMs) for domain-specific retrieval tasks. ConFIRM leverages the Five-Factor …
GPUInformation Retrievalintent-classificationIntent Classification+3CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss
This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the la…
image-classificationImage Classificationintent-classificationIntent Classification+1In-Context Learning for Text Classification with Many Labels
In-context learning (ICL) using large language models for tasks with many labels is challenging due to the limited context window, which makes it difficult to fit a sufficient number of examples in the prompt. In this pa…
ClassificationIn-Context Learningintent-classificationIntent Classification+5Leveraging Large Language Models for Exploiting ASR Uncertainty
While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on off-the-shelf automatic speech recognition…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+6Enhancing Pipeline-Based Conversational Agents with Large Language Models
The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and …
intent-classificationIntent ClassificationLanguage ModellingLarge Language Model+1Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification
Retrieval augmentation, which enhances downstream models by a knowledge retriever and an external corpus instead of by merely increasing the number of model parameters, has been successfully applied to many natural langu…
intent-classificationIntent ClassificationLanguage ModelingLanguage Modelling+4Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling
We study speech intent classification and slot filling (SICSF) by proposing to use an encoder pretrained on speech recognition (ASR) to initialize an end-to-end (E2E) Conformer-Transformer model, which achieves the new s…
intent-classificationIntent ClassificationIntent Classification and Slot FillingSelf-Supervised Learning+5ITALIC: An Italian Intent Classification Dataset
Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects. We introduce ITALIC, th…
Classificationintent-classificationIntent Classificationspeech-recognition+2Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount of labeled data. The current approach to…
intent-classificationIntent ClassificationIntent DetectionNatural Language UnderstandingReliable and Interpretable Drift Detection in Streams of Short Texts
Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful…
Change Point DetectionDrift Detectionintent-classificationIntent ClassificationCIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-trai…
intent-classificationIntent ClassificationIntent Classification and Slot FillingLanguage Modeling+5Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Intent classification (IC) plays an important role in task-oriented dialogue systems. However, IC models often generalize poorly when training without sufficient annotated examples for each user intent. We propose a nove…
Contrastive Learningintent-classificationIntent ClassificationTask-Oriented Dialogue SystemsTaDSE: Template-aware Dialogue Sentence Embeddings
Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost. However, directly annotating and gatheri…
Contrastive Learningintent-classificationIntent ClassificationSemantic Compression+6ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intell…
Diversityintent-classificationIntent ClassificationParaphrase Generation+1Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding
Recently, large pretrained language models have demonstrated strong language understanding capabilities. This is particularly reflected in their zero-shot and in-context learning abilities on downstream tasks through pro…
GPUIn-Context Learningintent-classificationIntent Classification+3The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation
End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine translation has been established as a powerf…
Abstractive Text SummarizationContinual Learningintent-classificationIntent Classification+5Improving End-to-End SLU performance with Prosodic Attention and Distillation
Most End-to-End SLU methods depend on the pretrained ASR or language model features for intent prediction. However, other essential information in speech, such as prosody, is often ignored. Recent research has shown impr…
intent-classificationIntent ClassificationLanguage ModelingLanguage ModellingExploring Zero and Few-shot Techniques for Intent Classification
Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well…
ClassificationData AugmentationDomain Adaptationintent-classification+3