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Slot Filling
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The goal of Slot Filling is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.
Source: Real-time On-Demand Crowd-powered Entity Extraction
Image credit: Robust Retrieval Augmented Generation for Zero-shot Slot Filling
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Co-guiding for Multi-intent Spoken Language Understanding
Speech-based Slot Filling using Large Language Models
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
$R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
SALMONN: Towards Generic Hearing Abilities for Large Language Models
Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti
DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling
Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis
Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding
Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations
I$^2$KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding
Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Continual Learning with Dirichlet Generative-based Rehearsal
Introducing "Forecast Utterance" for Conversational Data Science
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
Task Conditioned BERT for Joint Intent Detection and Slot-filling
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting
Extracting Multi-valued Relations from Language Models
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling
Improved NL2SQL based on Multi-layer Expert Network
Enhancing Low Resource NER Using Assisting Language And Transfer Learning
Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction
TaDSE: Template-aware Dialogue Sentence Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.