Text Classification On Trec 6

Métriques

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Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
TM-Glove	9.96	Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
byte mLSTM7	9.6	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
DELTA (CNN)	7.8	DELTA: A DEep learning based Language Technology plAtform
SWEM-aver	7.8	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Capsule-B	7.2	Investigating Capsule Networks with Dynamic Routing for Text Classification
STM+TSED+PT+2L	7.04	The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning
GRU-RNN-GLOVE	7.0	All-but-the-Top: Simple and Effective Postprocessing for Word Representations
MPAD-path	6.2	Message Passing Attention Networks for Document Understanding
VLAWE	5.8	Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
C-LSTM	5.4	A C-LSTM Neural Network for Text Classification
CoVe	4.2	Learned in Translation: Contextualized Word Vectors
CNN+MCFA	4	Translations as Additional Contexts for Sentence Classification
TBCNN	4	Discriminative Neural Sentence Modeling by Tree-Based Convolution
LSTM-CNN	3.9	Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
ULMFiT	3.6	Universal Language Model Fine-tuning for Text Classification
BERT-ITPT-FiT	3.2	How to Fine-Tune BERT for Text Classification?
RoBERTa+DualCL	2.60	Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
USE_T+CNN	1.93	Universal Sentence Encoder
Automatic Label Error Correction	0.40	The Re-Label Method For Data-Centric Machine Learning

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Text Classification On Trec 6 | SOTA | HyperAI