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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
MLDoc
Latest papers
Bridging the domain gap in cross-lingual document classification
We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language.
Cross-Lingual Sentiment Quantification
Cross-lingual sentiment quantification (and cross-lingual \emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
sInceraSs/ELSA
•
•
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
jbarnesspain/blse
•
•
ACL 2018
Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models.
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification
To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists.
