Linear Transformations for Cross-lingual Sentiment Analysis
This paper deals with cross-lingual sentiment analysis in Czech, English and French languages. We perform zero-shot cross-lingual classification using five linear transformations combined with LSTM and CNN based classifiers. We compare the performance of the individual transformations, and in addition, we confront the transformation-based approach with existing state-of-the-art BERT-like models. We show that the pre-trained embeddings from the target domain are crucial to improving the cross-lingual classification results, unlike in the monolingual classification, where the effect is not so distinctive.
Code (1)
Tasks
ClassificationSentiment AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Cross-Lingual Sentiment Analysis Without (Good) Translation
Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it …
Machine TranslationSentiment AnalysisTranslationCross-lingual Word Analogies using Linear Transformations between Semantic Spaces
We generalize the word analogy task across languages, to provide a new intrinsic evaluation method for cross-lingual semantic spaces. We experiment with six languages within different language families, including English…
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models
Sentiment analysis serves as a pivotal component in Natural Language Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R and mT5 have contributed to the increasing interest in cross-lingual se…
Cross-Lingual TransferSentiment AnalysisXLM-RLanguage Fusion for Parameter-Efficient Cross-lingual Transfer
Limited availability of multilingual text corpora for training language models often leads to poor performance on downstream tasks due to undertrained representation spaces for languages other than English. This 'under-r…
Cross-Lingual TransferNatural Language InferenceNatural Language UnderstandingQuestion Answering+1Cross-lingual alignments of ELMo contextual embeddings
Building machine learning prediction models for a specific NLP task requires sufficient training data, which can be difficult to obtain for less-resourced languages. Cross-lingual embeddings map word embeddings from a le…
Dependency Parsingnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3