Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications
Unsupervised speech models are becoming ubiquitous in the speech and machine learning communities. Upstream models are responsible for learning meaningful representations from raw audio. Later, these representations serv…
Emotion Recognitionintent-classificationIntent ClassificationKeyword SpottingReMask: A Robust Information-Masking Approach for Domain Counterfactual Generation
Domain shift is a big challenge in NLP, thus, many approaches resort to learning domain-invariant features to mitigate the inference phase domain shift. Such methods, however, fail to leverage the domain-specific nuances…
counterfactualDomain Adaptationintent-classificationIntent Classification+4ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development
Existing medical text datasets usually take the form of ques- tion and answer pairs that support the task of natural language gener- ation, but lacking the composite annotations of the medical terms. In this study, we pu…
intent-classificationIntent Classificationnamed-entity-recognitionNamed Entity Recognition+3CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the…
ARCCitation Intent Classificationintent-classificationIntent Classification+1Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. …
Contrastive Learningintent-classificationIntent ClassificationEfficient Sequence Transduction by Jointly Predicting Tokens and Durations
This paper introduces a novel Token-and-Duration Transducer (TDT) architecture for sequence-to-sequence tasks. TDT extends conventional RNN-Transducer architectures by jointly predicting both a token and its duration, i.…
Intent ClassificationIntent Classification and Slot FillingSlot FillingSpeech Intent Classification+1Unsupervised Speech Representation Pooling Using Vector Quantization
With the advent of general-purpose speech representations from large-scale self-supervised models, applying a single model to multiple downstream tasks is becoming a de-facto approach. However, the pooling problem remain…
Emotion Recognitionintent-classificationIntent ClassificationKeyword Spotting+2Ericson: An Interactive Open-Domain Conversational Search Agent
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effect…
Conversational SearchDialogue ManagementInformation Retrievalintent-classification+4Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the h…
Cross-Lingual Transferintent-classificationIntent Classificationslot-filling+2A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisit…
intent-classificationIntent ClassificationRepresentation LearningRetrievalImproving the Intent Classification accuracy in Noisy Environment
Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-e…
Automatic Speech RecognitionClassificationintent-classificationIntent Classification+4Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. …
Decision MakingDeep Reinforcement Learningintent-classificationIntent ClassificationConversation Style Transfer using Few-Shot Learning
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer in…
Few-Shot LearningIn-Context Learningintent-classificationIntent Classification+3Skit-S2I: An Indian Accented Speech to Intent dataset
Conventional conversation assistants extract text transcripts from the speech signal using automatic speech recognition (ASR) and then predict intent from the transcriptions. Using end-to-end spoken language understandin…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+4Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction
When a human communicates with a machine using natural language on the web and online, how can it understand the human's intention and semantic context of their talk? This is an important AI task as it enables the machin…
Deep Learningintent-classificationIntent ClassificationIntent Classification and Slot Filling+6Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks
In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup. We benchmark three types of systems to perform …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+4The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition
Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge. It is common to have NLU systems limited to a subset of languages due to lack of available da…
intent-classificationIntent ClassificationNatural Language Understandingslot-filling+1Zero-Shot Learning for Joint Intent and Slot Labeling
It is expensive and difficult to obtain the large number of sentence-level intent and token-level slot label annotations required to train neural network (NN)-based Natural Language Understanding (NLU) components of task…
intent-classificationIntent ClassificationNatural Language UnderstandingSentence+4ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling
Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. One of the promising s…
intent-classificationIntent ClassificationIntent Classification and Slot FillingNatural Language Understanding+3Multitask Learning for Low Resource Spoken Language Understanding
We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification. Our models, althou…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Classificationintent-classification+6