Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems
Intent Recognition and Slot Identification are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these tasks in the context of low resourced and un…
Data AugmentationGeneral Classificationintent-classificationIntent Classification+3The impact of domain-specific representations on BERT-based multi-domain spoken language understanding
This paper provides the first experimental study on the impact of using domain-specific representations on a BERT-based multi-task spoken language understanding (SLU) model for multi-domain applications. Our results on a…
Classificationdomain classificationintent-classificationIntent Classification+4Industry Scale Semi-Supervised Learning for Natural Language Understanding
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We in…
intent-classificationIntent ClassificationKnowledge Distillationnamed-entity-recognition+5NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu
The understanding of the human language is quantified by identifying intents and entities. Even though classification methods that rely on labeled information are often used for the comprehension of language understandin…
Chatbotintent-classificationIntent ClassificationResponse GenerationLeveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification
Intent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module followed by text processing that classifies…
ClassificationGeneral Classificationintent-classificationIntent Classification+6Neural Data Augmentation via Example Extrapolation
In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such "few-shot" cases at test time. A common remedy is to perform …
Data AugmentationDiversityFew-Shot Learningintent-classification+4Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript
Recent years have witnessed significant improvement in ASR systems to recognize spoken utterances. However, it is still a challenging task for noisy and out-of-domain data, where substitution and deletion errors are prev…
intent-classificationIntent ClassificationLanguage ModelingLanguage ModellingProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification
Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this problem have focused on transfer learning u…
ClassificationData AugmentationGeneral Classificationintent-classification+8A survey of joint intent detection and slot-filling models in natural language understanding
Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent cla…
ClassificationGeneral Classificationintent-classificationIntent Classification+5A character representation enhanced on-device Intent Classification
Intent classification is an important task in natural language understanding systems. Existing approaches have achieved perfect scores on the benchmark datasets. However they are not suitable for deployment on low-resour…
ClassificationGeneral Classificationintent-classificationIntent Classification+1Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection
Real-life applications, heavily relying on machine learning, such as dialog systems, demand out-of-domain detection methods. Intent classification models should be equipped with a mechanism to distinguish seen intents fr…
intent-classificationIntent ClassificationIntent DetectionOut of Distribution (OOD) DetectionIntent Classification and Slot Filling for Privacy Policies
Understanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, and the constituent t…
General Classificationintent-classificationIntent ClassificationSentence+1Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning
Query reformulation aims to alter noisy or ambiguous text sequences into coherent ones closer to natural language questions. This is to prevent errors from propagating in a client-facing pipeline and promote better commu…
intent-classificationIntent ClassificationQuestion Answeringreinforcement-learning+2Deep Open Intent Classification with Adaptive Decision Boundary
Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without …
ClassificationGeneral Classificationintent-classificationIntent Classification+1Generation of complex database queries and API calls from natural language utterances
Generating queries corresponding to natural language questions is a long standing problem. Traditional methods lack language flexibility, while newer sequence-to-sequence models require large amount of data. Schema-agnos…
intent-classificationIntent ClassificationIntent Classification and Slot Fillingslot-filling+1Using multiple ASR hypotheses to boost i18n NLU performance
Current voice assistants typically use the best hypothesis yielded by their Automatic Speech Recognition (ASR) module as input to their Natural Language Understanding (NLU) module, thereby losing helpful information that…
Abstractive Text SummarizationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)domain classification+10Delexicalized Paraphrase Generation
We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of r…
Data Augmentationintent-classificationIntent Classificationnamed-entity-recognition+4STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++
Slot-filling, Translation, Intent classification, and Language identification, or STIL, is a newly-proposed task for multilingual Natural Language Understanding (NLU). By performing simultaneous slot filling and translat…
Classificationintent-classificationIntent ClassificationLanguage Identification+4Attentively Embracing Noise for Robust Latent Representation in BERT
Modern digital personal assistants interact with users through voice. Therefore, they heavily rely on automatic speech recognition (ASR) in order to convert speech to text and perform further tasks. We introduce EBERT, w…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)ChatbotClassification+7Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems
Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling. However, in evo…
intent-classificationIntent ClassificationParaphrase Generation