Papers by Dimitar Shterionov
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that existing models amplify biases observed in training data. |
| Approach: | They propose to use MT and NLP to amplify biases observed in training data to investigate how bias amplification might affect language in a broader sense. |
| Outcome: | The proposed model amplifys biases observed in training data and could lead to an artificially impoverished language, the authors show. |
Challenges with Sign Language Datasets for Sign Language Recognition and Translation (2022.lrec-1)
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Mirella De Sisto, Vincent Vandeghinste, Santiago Egea Gómez, Mathieu De Coster, Dimitar Shterionov, Horacio Saggion
| Challenge: | Sign Languages are the primary means of communication for at least half a million people in Europe . however, the development of SL recognition and translation tools is slowed down by resource scarcity and data formats are not suitable for machine learning. |
| Approach: | They propose a framework to unify available resources and facilitate SL research for different languages. |
| Outcome: | The proposed framework is based on a set of ELAN files and returns textual and visual data ready to train SL recognition and translation models. |
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation (2020.acl-main)
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| Challenge: | incorporating backtranslated data from different sources has led to improved results in machine translation (MT) |
| Approach: | They use a low-resource use-case and a high-resourced language pair to test different backtranslation scenarios and employ data selection to optimise the synthetic corpora. |
| Outcome: | The proposed method reduces the amount of data used while maintaining high-quality MT systems. |
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives (2021.emnlp-main)
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| Challenge: | Recent years have seen an increasing need for gender-neutral and inclusive language. |
| Approach: | They propose a rule-based and a neural approach to gender-neutral rewriting for English . they use manually curated synthetic and natural data to train a rewriter . |
| Outcome: | The proposed approach improves on the rule-based approach with word error rates below 0.18% on synthetic, in-domain and out-domain test sets. |