Papers with Code · CC BY-SA 4.0

Intent Recognition

Reading Comprehension › Intent Recognition 18 평가 기록 4 datasets

논문

MIntRec: A New Dataset for Multimodal Intent Recognition

2209.04355 2022-09-09 이 페이지의 평가 4건

Accuracy (20 classes): 85.51Accuracy (Binary): 94.72

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

1910.10683 2019-10-23 이 페이지의 평가 2건

F1: 58.9Precision: 54.1Recall: 64.6

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation

2211.05719 2022-11-10 이 페이지의 평가 2건

F1: 75.5

PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts

2305.14839 2023-05-24 이 페이지의 평가 2건

F1: 63.8Precision: 63.3Recall: 68

ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

2102.03334 2021-02-05 이 페이지의 평가 2건

F1: 52.4Precision: 55.4Recall: 58.9

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

1909.11942 2019-09-26 이 페이지의 평가 1건

F1: 52.2Precision: 44.8Recall: 62.7

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

1810.04805 2018-10-11 이 페이지의 평가 1건

F1: 53.2Precision: 56.1Recall: 50.6

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

2112.11446 2021-12-08 이 페이지의 평가 1건

Accuracy : 88.7

Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment

2305.11579 2023-05-19 이 페이지의 평가 1건

Accuracy (20 classes): 73.48

Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition

2312.14667 2023-12-22 이 페이지의 평가 1건

Accuracy (20 classes): 73.62

Training Compute-Optimal Large Language Models

2203.15556 2022-03-29 이 페이지의 평가 1건

Accuracy : 92.8