Papers by Nora Aranberri
Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| Challenge: | Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs. |
| Approach: | They propose to use statistical word alignments to project annotations from multiple sources to a target language. |
| Outcome: | The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language . |
TANDO: A Corpus for Document-level Machine Translation (2022.lrec-1)
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Harritxu Gete, Thierry Etchegoyhen, David Ponce, Gorka Labaka, Nora Aranberri, Ander Corral, Xabier Saralegi, Igor Ellakuria, Maite Martin
| Challenge: | Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information. |
| Approach: | They propose to use document-level corpus for Basque-Spanish language pairs to take into account contextual information and perform fine-grained evaluations of gender and gender. |
| Outcome: | The proposed corpus is suitable for fine-grained evaluation of document-level machine translation systems. |
COMET for Low-Resource Machine Translation Evaluation: A Case Study of English-Maltese and Spanish-Basque (2024.lrec-main)
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| Challenge: | Trainable metrics for machine translation evaluation have been scoring the highest correlations with human judgements in the meta-evaluations. |
| Approach: | They run a crowd-based evaluation campaign to evaluate COMET-22 and fine-tune it to improve its performance. |
| Outcome: | The proposed system outperforms BLEU and other lexical overlap metrics in the meta-evaluations. |