Papers with downstream sentiment analysis task
Cross-lingual Few-Shot Learning on Unseen Languages (2022.aacl-main)
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| Challenge: | Large pre-trained language models have demonstrated the ability to obtain good performance on downstream tasks with limited examples in resource-rich languages. |
| Approach: | They propose to use a downstream sentiment analysis task to analyze the effectiveness of several few-shot learning strategies across 12 languages, including 8 unseen languages, to compare results. |
| Outcome: | The proposed model, XLM-R, gives the best performance on a task with few examples in resource-rich languages. |
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling (N19-1)
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| Challenge: | Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). |
| Approach: | They propose a new subtask for Aspect Based Sentiment Analysis to extract opinion words as pairs from a given opinion target. |
| Outcome: | The proposed model outperforms existing methods significantly on several popular ABSA benchmarks. |
Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023.emnlp-main)
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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
| Challenge: | Currently, studies on bias and fairness in natural language processing focus on a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across languages for individual attributes. |
| Approach: | They adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for race, religion, nationality, and gender. |
| Outcome: | The proposed model favors groups that are dominant in each language's culture, indicating bias amplification, after multilingual finetuning. |