Papers Intent Classification and Slot Filling
“Intent Classification and Slot Filling” 태그가 달린 논문 33편 · 필터 해제
Building 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+4New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fifteen years, models achieving both of th…
intent-classificationIntent ClassificationIntent Classification and Slot Fillingslot-filling+2Prompt Perturbation Consistency Learning for Robust Language Models
Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However, their performance on sequence labeling t…
Data Augmentationintent-classificationIntent ClassificationIntent Classification and Slot Filling+4Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled dat…
intent-classificationIntent ClassificationIntent Classification and Slot FillingRelation+2Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling
We study speech intent classification and slot filling (SICSF) by proposing to use an encoder pretrained on speech recognition (ASR) to initialize an end-to-end (E2E) Conformer-Transformer model, which achieves the new s…
intent-classificationIntent ClassificationIntent Classification and Slot FillingSelf-Supervised Learning+5CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-trai…
intent-classificationIntent ClassificationIntent Classification and Slot FillingLanguage Modeling+5Efficient 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+1Spoken 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+6ESIE-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+3Augmenting 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+4CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with signi…
intent-classificationIntent ClassificationIntent Classification and Slot FillingIntent Detection+5A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog
Interest in dialog systems has grown substantially in the past decade. By extension, so too has interest in developing and improving intent classification and slot-filling models, which are two components that are common…
Classificationintent-classificationIntent ClassificationIntent Classification and Slot Filling+3Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such sy…
Contrastive LearningData AugmentationFew-Shot Learningintent-classification+6Local-to-global learning for iterative training of production SLU models on new features
In production SLU systems, new training data becomes available with time so that ML models need to be updated on a regular basis. Specifically, releasing new features adds new classes of data while the old data remains c…
intent-classificationIntent ClassificationIntent Classification and Slot Fillingslot-filling+1Bi-directional Joint Neural Networks for Intent Classification and Slot Filling
Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slo…
Classificationintent-classificationIntent ClassificationIntent Classification and Slot Filling+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+3An 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+3Semi-Supervised Few-Shot Intent Classification and Slot Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such sy…
ClassificationContrastive LearningData Augmentationintent-classification+6InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Natural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an approach to zero-shot…
intent-classificationIntent ClassificationIntent Classification and Slot FillingNatural Language Understanding+3CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Traditional toxicity detection models have focused on the single utterance level without deeper understanding of context. We introduce CONDA, a new dataset for in-game toxic language detection enabling joint intent class…
Dota 2intent-classificationIntent ClassificationIntent Classification and Slot Filling+3