Papers by Erick Fonseca
Revisiting Higher-Order Dependency Parsers (2020.acl-main)
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| Challenge: | Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parser. |
| Approach: | They found that neural parsers may benefit from higher-order features when employing a powerful pre-trained encoder, such as BERT. |
| Outcome: | Using a pre-trained encoder, we found that higher-order models are more accurate on full sentence parses and match of modifier lists. |
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset (2022.lrec-1)
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Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, André F. T. Martins
| Challenge: | Existing datasets for machine translation quality estimation and post-editing have several shortcomings. |
| Approach: | They propose a dataset for machine translation quality estimation and automatic post-editing . they report the performance of baseline systems trained on the MLQE-PE dataset . |
| Outcome: | The proposed dataset contains human labels for up to 10,000 translations per language pair. |