Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
In a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR) module as the input. The NLU module us…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+4Stacked DeBERT: All Attention in Incomplete Data for Text Classification
In this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, b…
AllChatbotClassificationDenoising+7Fast Intent Classification for Spoken Language Understanding
Spoken Language Understanding (SLU) systems consist of several machine learning components operating together (e.g. intent classification, named entity recognition and resolution). Deep learning models have obtained stat…
ClassificationDecision MakingGeneral Classificationintent-classification+6A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features
The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representation…
Activity Recognitionintent-classificationIntent ClassificationTransfer LearningA Joint Learning Framework With BERT for Spoken Language Understanding
Intent classification and slot filling are two essential tasks for spoken language understanding. Recently, joint learning has been shown to be effective for the two tasks. However, most joint learning methods only consi…
Decoderintent-classificationIntent ClassificationIntent Classification and Slot Filling+4Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification …
intent-classificationIntent ClassificationMulti-Task LearningParaphrase Identification+4ConveRT: Efficient and Accurate Conversational Representations from Transformers
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Repr…
Conversational Response Selectionintent-classificationIntent ClassificationQuantization+2Interactive Classification by Asking Informative Questions
We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks …
ClassificationGeneral Classificationintent-classificationIntent ClassificationMetric Learning for Dynamic Text Classification
Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in intent classification, new intents may be …
ClassificationGeneral Classificationintent-classificationIntent Classification+3Reconstructing Capsule Networks for Zero-shot Intent Classification
Intent classification is an important building block of dialogue systems. With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot i…
ClassificationGeneral Classificationintent-classificationIntent Classification+1Cross-lingual intent classification in a low resource industrial setting
This paper explores different approaches to multilingual intent classification in a low resource setting. Recent advances in multilingual text representations promise cross-lingual transfer for classifiers. We investigat…
ClassificationCross-Lingual TransferGeneral Classificationintent-classification+1RNN based Incremental Online Spoken Language Understanding
Spoken Language Understanding (SLU) typically comprises of an automatic speech recognition (ASR) followed by a natural language understanding (NLU) module. The two modules process signals in a blocking sequential fashion…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Blockingintent-classification+7Iterative Delexicalization for Improved Spoken Language Understanding
Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding and dialog systems. However, these mode…
intent-classificationIntent ClassificationSpoken Language UnderstandingA Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification
New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful o…
Data AugmentationGeneral Classificationintent-classificationIntent Classification+2Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents
Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular c…
Data AugmentationDecoderintent-classificationIntent Classification+1CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots
Natural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks - intent classification (IC) and slot labeling (SL), which are then followed by a dialogue management (DM) compo…
Dialogue Managementintent-classificationIntent ClassificationManagement+1Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language …
intent-classificationIntent ClassificationSemantic SimilaritySemantic Textual Similarity+4An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset t…
BenchmarkingGeneral Classificationintent-classificationIntent Classification+1Real-world Conversational AI for Hotel Bookings
In this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning mo…
BIG-bench Machine LearningChatbotDialogue ManagementGeneral Classification+8Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning
Natural Language Understanding (NLU) models are typically trained in a supervised learning framework. In the case of intent classification, the predicted labels are predefined and based on the designed annotation schema …
intent-classificationIntent ClassificationNatural Language Understanding