Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Exploring Robustness of Multilingual LLMs on Real-World Noisy Data
Large Language Models (LLMs) are trained on Web data that might contain spelling errors made by humans. But do they become robust to similar real-world noise? In this paper, we investigate the effect of real-world spelli…
intent-classificationIntent ClassificationNatural Language InferenceNERJoint Automatic Speech Recognition And Structure Learning For Better Speech Understanding
Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not…
Automatic Speech Recognitionintent-classificationIntent ClassificationNER+3Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding
While recent multilingual automatic speech recognition models claim to support thousands of languages, ASR for low-resource languages remains highly unreliable due to limited bimodal speech and text training data. Better…
Automatic Speech RecognitionClassificationintent-classificationIntent Classification+8Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study
Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages generally perform well…
intent-classificationIntent ClassificationIntent DetectionLanguage Modelling+9Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification
Dialogue intent classification aims to identify the underlying purpose or intent of a user's input in a conversation. Current intent classification systems encounter considerable challenges, primarily due to the vast num…
In-Context Learningintent-classificationIntent ClassificationLanguage Modeling+2Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary
Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods…
Classificationintent-classificationIntent ClassificationRepresentation LearningIntent-Aware Dialogue Generation and Multi-Task Contrastive Learning for Multi-Turn Intent Classification
Generating large-scale, domain-specific, multilingual multi-turn dialogue datasets remains a significant hurdle for training effective Multi-Turn Intent Classification models in chatbot systems. In this paper, we introdu…
ChatbotClassificationContrastive LearningDialogue Generation+2Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production
Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexity of contextual dependencies across dial…
ClassificationData Augmentationintent-classificationIntent ClassificationPredicting User Intents and Musical Attributes from Music Discovery Conversations
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in th…
AttributeClassificationintent-classificationIntent ClassificationImproved intent classification based on context information using a windows-based approach
Conversational systems have a Natural Language Understanding (NLU) module. In this module, there is a task known as an intent classification that aims at identifying what a user is attempting to achieve from an utterance…
Classificationintent-classificationIntent ClassificationNatural Language UnderstandingBuilding Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance l…
Cross-Lingual TransferDecoderDialogue Understandingintent-classification+4A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks
In virtual assistant (VA) systems it is important to reject or redirect user queries that fall outside the scope of the system. One of the most accurate approaches for out-of-scope (OOS) rejection is to combine it with t…
Classificationintent-classificationIntent ClassificationSentence+2Intent Classification for Bank Chatbots through LLM Fine-Tuning
This study evaluates the application of large language models (LLMs) for intent classification within a chatbot with predetermined responses designed for banking industry websites. Specifically, the research examines the…
Chatbotintent-classificationIntent ClassificationAre Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions
Classification is a core NLP task architecture with many potential applications. While large language models (LLMs) have brought substantial advancements in text generation, their potential for enhancing classification t…
Classificationintent-classificationIntent ClassificationText GenerationEfficacy of Synthetic Data as a Benchmark
Large language models (LLMs) have enabled a range of applications in zero-shot and few-shot learning settings, including the generation of synthetic datasets for training and testing. However, to reliably use these synth…
BenchmarkingFew-Shot Learningintent-classificationIntent Classification+2Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection
In the realm of task-oriented dialogue systems, a robust intent detection mechanism must effectively handle malformed utterances encountered in real-world scenarios. This study presents a novel fine-tuning framework for …
Diversityintent-classificationIntent ClassificationIntent Detection+1Uddessho: An Extensive Benchmark Dataset for Multimodal Author Intent Classification in Low-Resource Bangla Language
With the increasing popularity of daily information sharing and acquisition on the Internet, this paper introduces an innovative approach for intent classification in Bangla language, focusing on social media posts where…
intent-classificationIntent ClassificationLLM-based Weak Supervision Framework for Query Intent Classification in Video Search
Streaming services have reshaped how we discover and engage with digital entertainment. Despite these advancements, effectively understanding the wide spectrum of user search queries continues to pose a significant chall…
In-Context Learningintent-classificationIntent ClassificationNatural Language Understanding+1LLMs Will Always Hallucinate, and We Need to Live With This
As Large Language Models become more ubiquitous across domains, it becomes important to examine their inherent limitations critically. This work argues that hallucinations in language models are not just occasional error…
Fact CheckingHallucinationintent-classificationIntent Classification+1Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog System
The advancements in time-efficient data collection techniques such as active learning (AL) has become salient for user intent classification performance in task-oriented dialog systems (TODS). In realistic settings, howe…
Active LearningInformativenessintent-classificationIntent Classification+1