Papers Cross-Lingual Sentiment Classification

N2C2: Nearest Neighbor Enhanced Confidence Calibration for Cross-Lingual In-Context Learning

Recent advancements of in-context learning (ICL) show language models can significantly improve their performance when demonstrations are provided. However, little attention has been paid to model calibration and predict…

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. Therefore, we study gener…

Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification

Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks. However, there are still gaps between the contextualized representations of similar words in…

Cross-lingual sentiment classification in low-resource Bengali language

Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools. Therefore, in this work, we focus on generating resou…

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. Their success heavily depends on the availability of a large amount of labeled data or …

Bridging the domain gap in cross-lingual document classification

The scarcity of labeled training data often prohibits the internationalization of NLP models to multiple languages. Recent developments in cross-lingual understanding (XLU) has made progress in this area, trying to bridg…

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 ne…

Cross-Lingual Sentiment Quantification

\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of unlabelled documents) is an important to…

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any …

Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages

Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentim…

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. Almost all research into CLSC has been carried out at sente…

Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specifi…

Attention-based LSTM Network for Cross-Lingual Sentiment Classification

Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of …

Aspect-Level Cross-lingual Sentiment Classification with Constrained SMT