Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Word-Free Spoken Language Understanding for Mandarin-Chinese
Spoken dialogue systems such as Siri and Alexa provide great convenience to people's everyday life. However, current spoken language understanding (SLU) pipelines largely depend on automatic speech recognition (ASR) modu…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+6Representation based meta-learning for few-shot spoken intent recognition
Spoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or commands, which restricts the quick custom…
Classificationintent-classificationIntent ClassificationIntent Detection+2Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU
Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) ut…
intent-classificationIntent ClassificationOut of Distribution (OOD) DetectionSpoken Language UnderstandingCBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applicat…
Intent ClassificationMedical Concept NormalizationMedical Relation ExtractionNamed Entity Recognition+6CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Traditional toxicity detection models have focused on the single utterance level without deeper understanding of context. We introduce CONDA, a new dataset for in-game toxic language detection enabling joint intent class…
Dota 2intent-classificationIntent ClassificationIntent Classification and Slot Filling+3Are Pretrained 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 RecognitionOptimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may…
Entity Resolutionintent-classificationIntent ClassificationNatural Language Understanding+1From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, i…
intent-classificationIntent ClassificationIntent DetectionLanguage Modeling+6Meta-Inductive Node Classification across Graphs
Semi-supervised node classification on graphs is an important research problem, with many real-world applications in information retrieval such as content classification on a social network and query intent classificatio…
ClassificationGeneral KnowledgeInformation Retrievalintent-classification+4OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attack
Out-of-domain (OOD) input detection is vital in a task-oriented dialogue system since the acceptance of unsupported inputs could lead to an incorrect response of the system. This paper proposes OutFlip, a method to gener…
intent-classificationIntent ClassificationIntent DetectionJoint Text and Label Generation for Spoken Language Understanding
Generalization is a central problem in machine learning, especially when data is limited. Using prior information to enforce constraints is the principled way of encouraging generalization. In this work, we propose to le…
intent-classificationIntent ClassificationLearning with noisy labelsSpoken Language UnderstandingOut-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling
User queries for a real-world dialog system may sometimes fall outside the scope of the system's capabilities, but appropriate system responses will enable smooth processing throughout the human-computer interaction. Thi…
ClassificationGeneral Classificationintent-classificationIntent Classification+1Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System
Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled dat…
ClassificationFew-Shot Text ClassificationGeneral Classificationintent-classification+4Fuzzy Classification of Multi-intent Utterances
Current intent classification approaches assign binary intent class memberships to natural language utterances while disregarding the inherent vagueness in language and the corresponding vagueness in intent class boundar…
ClassificationGeneral Classificationintent-classificationIntent ClassificationAdapting Long Context NLM for ASR Rescoring in Conversational Agents
Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context. I…
intent-classificationIntent ClassificationLanguage ModellingNatural Language UnderstandingData Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase
We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare and evaluate this method with a range of…
Data Augmentationintent-classificationIntent ClassificationSemantic Similarity+3Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding
Most End-to-End (E2E) SLU networks leverage the pre-trained ASR networks but still lack the capability to understand the semantics of utterances, crucial for the SLU task. To solve this, recently proposed studies use pre…
intent-classificationIntent ClassificationIntent Classification and Slot FillingKnowledge Distillation+5On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise
Intent Classification (IC) and Slot Labeling (SL) models, which form the basis of dialogue systems, often encounter noisy data in real-word environments. In this work, we investigate how robust IC/SL models are to noisy …
Data AugmentationGoal-Oriented DialogGoal-Oriented Dialogue Systemsintent-classification+1Few-shot Intent Classification and Slot Filling with Retrieved Examples
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrie…
ClassificationFew-Shot LearningGeneral Classificationintent-classification+6Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs
A major focus of recent research in spoken language understanding (SLU) has been on the end-to-end approach where a single model can predict intents directly from speech inputs without intermediate transcripts. However, …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+3