MR

Overview edit

Variants: MR, rotten_tomatoes

Associated Benchmarks

This dataset is used in 3 benchmarks:

• Few-Shot Learning - Metrics: Acc
• Sentiment Analysis - Metrics: Accuracy, Training Time
• Text Classification - Metrics: Accuracy

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• AnglE-optimized Text Embeddings (2023) -
• Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets (2022) -
• Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners (2021) -
• BertGCN: Transductive Text Classification by Combining GCN and BERT (2021) -
• Entailment as Few-Shot Learner (2021) -
• Enhancing Interpretable Clauses Semantically using Pretrained Word Representation (2021) -
• Graph Star Net for Generalized Multi-Task Learning (2019) -
• The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning (2019) -
• Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation (2019) -
• Simplifying Graph Convolutional Networks (2019) -
• Graph Convolutional Networks for Text Classification (2018) -
• A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (2018) -
• Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (2018) -
• Improved Sentence Modeling using Suffix Bidirectional LSTM (2018) -
• A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018) -
• Sentence-State LSTM for Text Representation (2018) -
• Investigating Capsule Networks with Dynamic Routing for Text Classification (2018) -
• Universal Sentence Encoder (2018) -
• Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm (2017) -
• All-but-the-Top: Simple and Effective Postprocessing for Word Representations (2017) -