Summary of Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English, by Aekansh Kathunia et al.
Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English
by Aekansh Kathunia, Mohammad Kaif, Nalin Arora, N Narotam
First submitted to arxiv on: 5 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the performance of transformer-based models in Sentiment Analysis tasks on multilingual datasets and machine-translated text. The authors examine how these models fare in different linguistic contexts, shedding light on their variations and implications for sentiment analysis across diverse languages. Specifically, they analyze the effectiveness of transformer models like BERT and RoBERTa on datasets such as IMDB, Amazon Product Reviews, and XStandford Sentiment Treebank, which have been machine-translated into various languages. By doing so, they aim to bridge the gap in sentiment resources available for English and other languages, ultimately contributing to more inclusive and diverse natural language processing (NLP) research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computer models can understand people’s emotions when reading text written in different languages. Right now, most of these models are only good at understanding English text, which isn’t fair since there are so many other languages spoken around the world. The researchers tested some popular AI models on texts translated from other languages and found that they performed differently depending on the language. This helps us understand how we can make these models better at handling different languages in the future. |
Keywords
* Artificial intelligence * Bert * Natural language processing * Nlp * Transformer
