Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention
Currently, in spoken language understanding (SLU) systems, the automatic speech recognition (ASR) module produces multiple interpretations (or hypotheses) for the input audio signal and the natural language understanding…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+7Meta learning to classify intent and slot labels with noisy few shot examples
Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep learning models are notorious for being data-hungry, and the heavily optimized models are usu…
Benchmarkingintent-classificationIntent ClassificationMeta-Learning+1SLURP: A Spoken Language Understanding Resource Package
Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are …
Intent ClassificationSlot FillingSpoken Language UnderstandingSearch4Code: Code Search Intent Classification Using Weak Supervision
Developers use search for various tasks such as finding code, documentation, debugging information, etc. In particular, web search is heavily used by developers for finding code examples and snippets during the coding pr…
ClassificationCode SearchGeneral Classificationintent-classification+1Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages
With recent advancements in language technologies, humans are now speaking to devices. Increasing the reach of spoken language technologies requires building systems in local languages. A major bottleneck here are the un…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Cross-Lingual Transferintent-classification+4Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey
In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented…
intent-classificationIntent ClassificationNatural Language Understandingslot-filling+3Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes
This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph…
ClassificationGeneral ClassificationGraph Embeddingintent-classification+1Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness
Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more div…
Diversityintent-classificationIntent ClassificationIntent Classification and Slot Filling+2Empirical Studies of Institutional Federated Learning For Natural Language Processing
Federated learning has sparkled new interests in the deep learning society to make use of isolated data sources from independent institutes. With the development of novel training tools, we have successfully deployed fed…
Federated LearningGPUintent-classificationIntent Classification+1SciWING– A Software Toolkit for Scientific Document Processing
We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recov…
Citation Intent Classificationintent-classificationIntent ClassificationTransfer LearningExample-Driven Intent Prediction with Observers
A key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot set…
Attributeintent-classificationIntent ClassificationPrediction+2End to End Binarized Neural Networks for Text Classification
Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns. In particular, these networks require high expense…
BinarizationClassificationGeneral Classificationintent-classification+3Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is time consuming and expensive to collect.…
Data Augmentationintent-classificationIntent Classificationtext-to-speech+2STIL -- 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…
ClassificationGeneral Classificationintent-classificationIntent Classification+5一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention)
在人机对话中,系统需要通过意图分类判断用户意图,再触发相应的业务类型。由于多轮人机对话具有口语化、长文本和特征稀疏等特点,现有的文本分类方法在人机对话意图分类上还存在较大困难。本文在层次注意力网络(hierarchical attention networks, HAN)基础上,提出了一种结合话语伪标签注意力的层次注意力网络模型PLA-HAN (HAN with utterance pseudo label attention)。PLA…
intent-classificationIntent ClassificationPseudo LabelZero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples. We propose to learn class representations from common …
Common Sense ReasoningEntity Typingintent-classificationIntent Classification+2Augmented Natural Language for Generative Sequence Labeling
We propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space. Unlike pr…
intent-classificationIntent Classificationnamed-entity-recognitionNamed Entity Recognition+2A Comparison of LSTM and BERT for Small Corpus
Recent advancements in the NLP field showed that transfer learning helps with achieving state-of-the-art results for new tasks by tuning pre-trained models instead of starting from scratch. Transformers have made a signi…
intent-classificationIntent Classificationtext-classificationText Classification+2Emora: An Inquisitive Social Chatbot Who Cares For You
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the curre…
Chatbotintent-classificationIntent ClassificationSimple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification
Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeabl…
Data AugmentationGeneral Classificationintent-classificationIntent Classification+3