Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with signi…
intent-classificationIntent ClassificationIntent Classification and Slot FillingIntent Detection+5LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging
We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual sequence-to-sequence (seq2seq) model, on a fl…
intent-classificationIntent ClassificationLanguage ModelingLanguage Modelling+2Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification
The use of deep neural networks in electromyogram (EMG) based prostheses control provides a promising alternative to the hand-crafted features by automatically learning muscle activation patterns from the EMG signals. Me…
intent-classificationIntent ClassificationGeneralized Intent Discovery: Learning from Open World Dialogue System
Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent classes. But users may input out-of-domain (OOD) queries in a practical dialogue system. Su…
intent-classificationIntent ClassificationIntent DiscoveryData Augmentation for Intent Classification of German Conversational Agents in the Finance Domain
Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems
A Natural Language Understanding (NLU) component can be used in a dialogue system to perform intent classification, returning an N-best list of hypotheses with corresponding confidence estimates. We perform an in-depth e…
Classificationintent-classificationIntent ClassificationNatural Language UnderstandingZ-BERT-A: a zero-shot Pipeline for Unknown Intent detection
Intent discovery is a crucial task in natural language processing, and it is increasingly relevant for various of industrial applications. Identifying novel, unseen intents from user inputs remains one of the biggest cha…
intent-classificationIntent ClassificationIntent DetectionIntent Discovery+1A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog
Interest in dialog systems has grown substantially in the past decade. By extension, so too has interest in developing and improving intent classification and slot-filling models, which are two components that are common…
Classificationintent-classificationIntent ClassificationIntent Classification and Slot Filling+3A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking
Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of diverse services to improve model robustn…
Dialogue State TrackingIntent Classificationslot-fillingSlot Filling+3Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms
Current virtual assistant (VA) platforms are beholden to the limited number of languages they support. Every component, such as the tokenizer and intent classifier, is engineered for specific languages in these intricate…
BenchmarkingClassificationintent-classificationIntent ClassificationStrategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such sy…
Contrastive LearningData AugmentationFew-Shot Learningintent-classification+6Controlled Data Generation via Insertion Operations for NLU
Use of synthetic data is rapidly emerging as a realistic alternative to manually annotating live traffic for industry-scale model building. Manual data annotation is slow, expensive and not preferred for meeting customer…
intent-classificationIntent Classificationnamed-entity-recognitionNamed Entity Recognition+2Local-to-global learning for iterative training of production SLU models on new features
In production SLU systems, new training data becomes available with time so that ML models need to be updated on a regular basis. Specifically, releasing new features adds new classes of data while the old data remains c…
intent-classificationIntent ClassificationIntent Classification and Slot Fillingslot-filling+1Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models
In Spoken Language Understanding (SLU) the task is to extract important information from audio commands, like the intent of what a user wants the system to do and special entities like locations or numbers. This paper pr…
Intent ClassificationSlot FillingSpeech-to-TextSpoken Language UnderstandingAlexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, …
Cross-Lingual Natural Language Inferenceintent-classificationIntent ClassificationNatural Language Inference+4Data Augmentation for Intent Classification
Training accurate intent classifiers requires labeled data, which can be costly to obtain. Data augmentation methods may ameliorate this issue, but the quality of the generated data varies significantly across techniques…
ClassificationData Augmentationintent-classificationIntent ClassificationA Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism
Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data. To obtain more satisfactory perfor…
Classificationintent-classificationIntent ClassificationMeta-Learning+3Learning Dialogue Representations from Consecutive Utterances
Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dial…
Contrastive LearningConversational Question AnsweringConversational Response SelectionDialogue Act Classification+14On Building Spoken Language Understanding Systems for Low Resourced Languages
Spoken dialog systems are slowly becoming and integral part of the human experience due to their various advantages over textual interfaces. Spoken language understanding (SLU) systems are fundamental building blocks of …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+3Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks
This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required. Although recent work has shown that there can be s…
intent-classificationIntent Classification