Papers by Lisa Yankovskaya
A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Using neural paraphrasing techniques, we investigate whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations. |
| Approach: | They propose to use neural paraphrasing techniques to generate additional references that provide better coverage of the space of valid translations. |
| Outcome: | The proposed approach beats human paraphrases in the BLEU evaluation. |
Unsupervised Quality Estimation for Neural Machine Translation (2020.tacl-1)
Copied to clipboard
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia
| Challenge: | Existing approaches require large amounts of expert annotated data, computation, and time for training. |
| Approach: | They propose an unsupervised approach to QE where no training is required . they use a dataset that enables work on both black-box and glass-box approaches . |
| Outcome: | The proposed approach rivals state-of-the-art supervised QE models in terms of correlation with human judgments of quality. |
Backtranslation Feedback Improves User Confidence in MT, Not Quality (2021.naacl-main)
Copied to clipboard
Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, Lisa Yankovskaya
| Challenge: | Inbound translation is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. |
| Approach: | They propose to provide cues that indicate the quality of MT output as well as suggest possible rephrasing of the source language. |
| Outcome: | The proposed feedback module increases user confidence in the produced translation, but not the objective quality. |