Papers Intent Classification
“Intent Classification” 태그가 달린 논문 375편 · 필터 해제
pNLP-Mixer: an Efficient all-MLP Architecture for Language
Large pre-trained language models based on transformer architecture have drastically changed the natural language processing (NLP) landscape. However, deploying those models for on-device applications in constrained devi…
Allintent-classificationIntent ClassificationSemantic Parsing+2mSLAM: Massively multilingual joint pre-training for speech and text
We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unlabeled speech and text in multiple langua…
cross-modal alignmentintent-classificationIntent ClassificationLanguage Modeling+4A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot
In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a…
Chatbotintent-classificationIntent ClassificationManagement+4Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF
Data sparsity problem is a key challenge of Natural Language Understanding (NLU), especially for a new target domain. By training an NLU model in source domains and applying the model to an arbitrary target domain direct…
intent-classificationIntent ClassificationIntent Classification and Slot FillingNatural Language Understanding+3Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets
Recent years have brought a tremendous growth in assistive robots/prosthetics for people with partial or complete loss of upper limb control. These technologies aim to help the users with various reaching and grasping ta…
intent-classificationIntent ClassificationTowards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques
Deep learning based methods have shown tremendous success in several Natural Language Processing (NLP) tasks. The recent trends in the usage of Deep Learning based models for natural language tasks have definitely produc…
intent-classificationIntent ClassificationIntent DetectionNatural Language UnderstandingTraining data reduction for multilingual Spoken Language Understanding systems
Fine-tuning self-supervised pre-trained language models such as BERT has significantly improved state-of-the-art performance on natural language processing tasks. Similar finetuning setups can also be used in commercial …
intent-classificationIntent ClassificationSpoken Language UnderstandingMulti-task pre-finetuning for zero-shot cross lingual transfer
Building machine learning models for low resource languages is extremely challenging due to the lack of available training data (either un-annotated or annotated). To support such scenarios, zero-shot cross lingual trans…
Cross-Lingual Transferintent-classificationIntent Classificationnamed-entity-recognition+4On Spoken Language Understanding Systems for Low Resourced Languages
Spoken dialog systems are slowly becoming and integral part of the human experience due to their various advantages over textual interfaces. Spoken language understanding (SLU) systems are fundamental building blocks of …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+3Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to e…
Dialogue State TrackingEnd-To-End Dialogue Modellingintent-classificationIntent ClassificationTowards Better Citation Intent Classification
Accurate classification of citation intents in a scientific article provides deeper contextual understanding of and better quantifies the contributions of cited articles. This improves scientific literature platform capa…
ArticlesCitation Intent ClassificationClassificationData Augmentation+3Class Embeddings for Improved Out-of-Scope Detection in Intent Classification
This work presents a deep investigation of {\emph class embeddings} for intent classification, which had only been timidly addressed before. We show that there are advantages in representing classes as dense vectors inst…
intent-classificationIntent ClassificationSentenceSentence EmbeddingsData Augmentation for Intent Classification with Generic Large Language Models
Data augmentation alleviates the problem of data scarcity when training language models (LMs) by generating new examples based on the existing data. A successful approach to generate new samples is to fine-tune a pretrai…
ClassificationData Augmentationintent-classificationIntent ClassificationA Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding
Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary progress in Automatic Speech Recognition (ASR). However, they have not been totally proven to produce better performance on tasks othe…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Emotion Recognitionintent-classification+8Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label
Zhang et al. (2020) proposed to formulate few-shot intent classification as natural language inference (NLI) between query utterances and examples in the training set. The method is known as discriminative nearest neighb…
Classificationintent-classificationIntent ClassificationNatural Language InferenceMultilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems
The lack of labeled training data for new features is a common problem in rapidly changing real-world dialog systems. As a solution, we propose a multilingual paraphrase generation model that can be used to generate nove…
intent-classificationIntent ClassificationMultilingual Paraphrase GenerationParaphrase GenerationNot So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification
Incremental intent classification requires the assignment of intent labels to partial utterances. However, partial utterances do not necessarily contain enough information to be mapped to the intent class of their comple…
Classificationintent-classificationIntent ClassificationAn Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling
Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems. These two tasks are closely-related and can flourish each other. Since only a few utterances can be utilize…
Contrastive Learningintent-classificationIntent ClassificationIntent Classification and Slot Filling+3Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages
Building Spoken Language Understanding (SLU) systems that do not rely on language specific Automatic Speech Recognition (ASR) is an important yet less explored problem in language processing. In this paper, we present a …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Decoderintent-classification+4MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants
In multimodal assistant, where vision is also one of the input modalities, the identification of user intent becomes a challenging task as visual input can influence the outcome. Current digital assistants take spoken in…
intent-classificationIntent ClassificationQuestion AnsweringQuestion Generation+3