Papers by Fabienne Braune
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages (2020.lrec-1)
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| Challenge: | Sentiment lexica are vital for sentiment analysis in absence of document-level annotations . linguistic resources are limited for at least a few hundred languages, putting them at risk of extinction . |
| Approach: | They introduce UniSent universal sentiment lexica for 1000+ languages . they use a Bible corpus to project sentiment information from English to other languages based on Twitter data . |
| Outcome: | The proposed method mitigates domain mismatch between Bible and Twitter by using embeddings . it compares to other sentiment seeding methods in a subset of languages with ground truth available . |
KnowMAN: Weakly Supervised Multinomial Adversarial Networks (2021.emnlp-main)
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| Challenge: | Existing approaches to weakly supervised training lack labeled data . weakly-supervised training can result in heuristic but noisy labels . |
| Approach: | They propose a scheme that allows to control influence of signals associated with specific labeling functions. |
| Outcome: | The proposed scheme improves results compared to weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. |
Evaluating bilingual word embeddings on the long tail (N18-2)
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| Challenge: | Bilingual word embeddings are useful for bilingual lexicon induction, but they focus on frequent words in general domains. |
| Approach: | They propose to evaluate bilingual word embeddings on rare words in different domains . they propose to use a multilingual dataset to build and combine BWEs based on a single word . |
| Outcome: | The proposed evaluations show that state-of-the-art methods fail on rare words . the proposed evaluation is based on a gold standard dataset and code . |
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (P18-1)
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| Challenge: | Previously, domain adaptation approaches to bilingual tasks were proposed . we show that simple adaptation process involving only unlabeled text is highly effective . |
| Approach: | They propose a method for domain adaptation of bilingual word embeddings using unlabeled data . they then tailor a semi-supervised classification method from computer vision to these tasks . |
| Outcome: | The proposed method improves on two bilingual tasks using unlabeled data. |