Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Fine-grained Intent Classification in the Legal Domain
A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document …
Classificationintent-classificationIntent ClassificationORCAS-I: Queries Annotated with Intent using Weak Supervision
User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation between navigational, transactional and info…
ClassificationInformation Retrievalintent-classificationIntent Classification+1Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks and then …
intent-classificationIntent ClassificationIntent DetectionIntent RecognitionKnowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains
Large Transformer-based natural language understanding models have achieved state-of-the-art performance in dialogue systems. However, scarce labeled data for training, the large model size, and low inference speed hinde…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot Learningintent-classification+3Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification
Open-world classification in dialog systems require models to detect open intents, while ensuring the quality of in-domain (ID) intent classification. In this work, we revisit methods that leverage distance-based statist…
intent-classificationIntent ClassificationOut of Distribution (OOD) DetectionLabel Errors in BANKING77
We investigate potential label errors present in the popular BANKING77 dataset and the associated negative impacts on intent classification methods. Motivated by our own negative results when constructing an intent class…
intent-classificationIntent ClassificationKNN-Contrastive Learning for Out-of-Domain Intent Classification
The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems. Previous methods commonly restrict the region (in feature space) of In-domain (IND) intent features to be compact or sim…
ClassificationContrastive Learningintent-classificationIntent Classification+2MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assista…
intent-classificationIntent ClassificationNatural Language UnderstandingSlot Filling+3Learning to Classify Open Intent via Soft Labeling and Manifold Mixup
Open intent classification is a practical yet challenging task in dialogue systems. Its objective is to accurately classify samples of known intents while at the same time detecting those of open (unknown) intents. Exist…
intent-classificationIntent ClassificationOutlier DetectionRedwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset
Dialog systems must be capable of incorporating new skills via updates over time in order to reflect new use cases or deployment scenarios. Similarly, developers of such ML-driven systems need to be able to add new train…
Classificationintent-classificationIntent ClassificationBuilding an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data
A Virtual Patient (VP) is a powerful tool for training medical students to take patient histories, where responding to a diverse set of spoken questions is essential to simulate natural conversations with a student. The …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+3Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model
In spoken language understanding (SLU), what the user says is converted to his/her intent. Recent work on end-to-end SLU has shown that accuracy can be improved via pre-training approaches. We revisit ideas presented by …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+3Quick Starting Dialog Systems with Paraphrase Generation
Acquiring training data to improve the robustness of dialog systems can be a painstakingly long process. In this work, we propose a method to reduce the cost and effort of creating new conversational agents by artificial…
intent-classificationIntent ClassificationMachine TranslationParaphrase GenerationData Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based approach to generate labelled training data for intent classification with off-the-sh…
Data Augmentationintent-classificationIntent ClassificationLightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
Self-supervised speech representation learning has shown promising results in various speech processing tasks. However, the pre-trained models, e.g., HuBERT, are storage-intensive Transformers, limiting their scope of ap…
AllAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classification+7A Speech Representation Anonymization Framework via Selective Noise Perturbation
Privacy and security are major concerns when communicating speech signals to cloud services such as automatic speech recognition (ASR) and speech emotion recognition (SER). Existing solutions for speech anonymization mai…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Emotion Recognitionintent-classification+7Towards Textual Out-of-Domain Detection without In-Domain Labels
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a challenging case of OOD detection, where no l…
Contrastive Learningintent-classificationIntent ClassificationLanguage Modeling+3Bi-directional Joint Neural Networks for Intent Classification and Slot Filling
Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slo…
Classificationintent-classificationIntent ClassificationIntent Classification and Slot Filling+4A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous directions for user intents. Before oper…
Active LearningData Augmentationintent-classificationIntent ClassificationWhen BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
The rapid development of quantum computing has demonstrated many unique characteristics of quantum advantages, such as richer feature representation and more secured protection on model parameters. This work proposes a v…
ClassificationCPUDecoderFederated Learning+7