Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recognition (ASR) to large language model (LLM)…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Entity Resolutionintent-classification+10Prompt Learning for Domain Adaptation in Task-Oriented Dialogue
Conversation designers continue to face significant obstacles when creating production quality task-oriented dialogue systems. The complexity and cost involved in schema development and data collection is often a major b…
Domain Adaptationintent-classificationIntent ClassificationLanguage Modeling+4Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures
Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficie…
intent-classificationIntent Classificationnamed-entity-recognitionNamed Entity Recognition+1token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text
Self-supervised pre-training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-text joint pre-t…
intent-classificationIntent ClassificationLanguage ModelingLanguage ModellingEnd-to-end Spoken Language Understanding with Tree-constrained Pointer Generator
End-to-end spoken language understanding (SLU) suffers from the long-tail word problem. This paper exploits contextual biasing, a technique to improve the speech recognition of rare words, in end-to-end SLU systems. Spec…
intent-classificationIntent Classificationslot-fillingSlot Filling+4arXivEdits: Understanding the Human Revision Process in Scientific Writing
Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance. However, prior work studying the human editing process in this domain mainly focused …
intent-classificationIntent ClassificationSentenceEnd-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+2Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation f…
Data AugmentationDialogue Understandingintent-classificationIntent ClassificationLearning Better Intent Representations for Financial Open Intent Classification
With the recent surge of NLP technologies in the financial domain, banks and other financial entities have adopted virtual agents (VA) to assist customers. A challenging problem for VAs in this domain is determining a us…
Classificationintent-classificationIntent ClassificationLanguage Modeling+2Augmenting Task-Oriented Dialogue Systems with Relation Extraction
The standard task-oriented dialogue pipeline uses intent classification and slot-filling to interpret user utterances. While this approach can handle a wide range of queries, it does not extract the information needed to…
intent-classificationIntent ClassificationIntent Classification and Slot FillingRelation+4Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR
Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, …
intent-classificationIntent ClassificationEnhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize …
Contrastive Learningintent-classificationIntent ClassificationLanguage Modeling+4The Open-World Lottery Ticket Hypothesis for OOD Intent Classification
Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms. However, they are underdeveloped in the underlying principle that the models shou…
intent-classificationIntent ClassificationThe Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification
Few-shot Intent Classification (FSIC) is one of the key challenges in modular task-oriented dialog systems. While advanced FSIC methods are similar in using pretrained language models to encode texts and nearest neighbou…
intent-classificationIntent ClassificationMeta-LearningKnowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks
Teacher-student knowledge distillation is a popular technique for compressing today's prevailing large language models into manageable sizes that fit low-latency downstream applications. Both the teacher and the choice o…
domain classificationintent-classificationIntent ClassificationKnowledge Distillation+3Explainable Abuse Detection as Intent Classification and Slot Filling
To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent po…
Abuse DetectionGeneral Classificationintent-classificationIntent Classification+1A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products
We present a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search. Following ELECTRA’s replaced token detection (RTD) pre-training, we leverage biomedical entity masking (EM) str…
Attributeintent-classificationIntent ClassificationLanguage Modeling+4Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings
Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users throu…
abstractive question answeringArticlesintent-classificationIntent Classification+1Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model
FAQs are important resources to find information. However, especially if a FAQ concerns many question-answer pairs, it can be a difficult and time-consuming job to find the answer you are looking for. A FAQ chatbot can e…
Chatbotintent-classificationIntent ClassificationTaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding
Meta-Learning has emerged as a research direction to better transfer knowledge from related tasks to unseen but related tasks. However, Meta-Learning requires many training tasks to learn representations that transfer we…
Data AugmentationDiversityintent-classificationIntent Classification+2