Cross-Lingual Sentiment Classification
6 papers with code • 4 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Cross-Lingual Sentiment Classification
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MLDoc
Latest papers with no code
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study
The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models.
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning
Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages.
On the Effect of Word Order on Cross-lingual Sentiment Analysis
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (RNNs) or convolutional networks (CNNs).
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.
Cross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language.
Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings
For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language.
