Papers Semantic Frame Parsing
“Semantic Frame Parsing” 태그가 달린 논문 23편 · 필터 해제
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous works train the model contrastively to …
Contrastive LearningData AugmentationIntent DetectionNatural Language Understanding+3MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations. Recent advanced approaches, which are joint models based on graph…
graph constructionIntent DetectionSemantic Frame Parsingslot-filling+1Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
Multiple intent detection and slot filling are two fundamental and crucial tasks in spoken language understanding. Motivated by the fact that the two tasks are closely related, joint models that can detect intents and ex…
Contrastive LearningIntent DetectionSemantic Frame Parsingslot-filling+2A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset
Most recent semantic frame parsing systems for spoken language understanding (SLU) are designed based on recurrent neural networks. These systems display decent performance on benchmark SLU datasets such as ATIS or SNIPS…
Semantic Frame ParsingSpoken Language UnderstandingEnhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
Multi-intent detection and slot filling joint model attracts more and more attention since it can handle multi-intent utterances, which is closer to complex real-world scenarios. Most existing joint models rely entirely …
Graph Neural NetworkIntent DetectionSemantic Frame Parsingslot-filling+1A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic inform…
Intent DetectionSemantic Frame Parsingslot-fillingSlot Filling+1A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations …
Intent DetectionSemantic Frame Parsingslot-fillingSlot Filling+1Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However, existi…
Graph AttentionIntent DetectionSemantic Frame Parsingslot-filling+1A Transformer-based Threshold-Free Framework for Multi-Intent NLU
Multi-intent natural language understanding (NLU) has recently gained attention. It detects multiple intents in an utterance, which is better suited to real-world scenarios. However, the state-of-the-art joint NLU models…
Intent DetectionMulti-Task LearningNatural Language UnderstandingSemantic Frame Parsing+1Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
The joint multiple Intent Detection (ID) and Slot Filling (SF) is a significant challenge in spoken language understanding. Because the slots in an utterance may relate to multi-intents, most existing approaches focus on…
Intent DetectionSemantic Frame Parsingslot-fillingSlot Filling+1Exploiting Topic Information for Joint Intent Detection and Slot Filling
Intent detection and slot filling are two important basic tasks in natural language understanding. Actually, there are multiple intents in an utterance. How to map different intents to corresponding slot becomes a new ch…
Intent DetectionNatural Language UnderstandingSemantic Frame Parsingslot-filling+1Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., in…
Intent DetectionSemantic Frame ParsingSentenceslot-filling+2SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
Utterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an ut…
Intent DetectionNatural Language UnderstandingSemantic Frame Parsingslot-filling+1An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/add/remove slot values through multiround …
Dialogue State TrackingSemantic Frame ParsingSentenceSpoken Language UnderstandingGL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow …
Intent DetectionSemantic Frame Parsingslot-fillingSlot FillingAnalyse automatique en cadres s\'emantiques pour l'apprentissage de mod\`eles de compr\'ehension de texte (Semantic Frame Parsing for training Machine Reading Comprehension models)
Dans le cadre de la compr{\'e}hension automatique de documents, cet article propose une {\'e}valuation intrins{\`e}que et extrins{\`e}que d{'}un mod{\`e}le d{'}analyse automatique en cadres s{\'e}mantiques (Frames). Le m…
Machine Reading ComprehensionReading ComprehensionSemantic Frame ParsingAGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporat…
Intent DetectionSemantic Frame ParsingSemantic Parsingslot-filling+2Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
This paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Domain GeneralizationSemantic Frame Parsing+5A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algori…
DecoderIntent Detectionmodel+4Sources of Complexity in Semantic Frame Parsing for Information Extraction
This paper describes a Semantic Frame parsing System based on sequence labeling methods, precisely BiLSTM models with highway connections, for performing information extraction on a corpus of French encyclopedic history …
Semantic Frame Parsing