198 citations · 452 across the 23 of their papers we have counts for
23 papers
Sign Language Translation with Sentence Embedding Supervision
Yasser Hamidullah, Josef van Genabith, Cristina España-Bonet
State-of-the-art sign language translation (SLT) systems facilitate the learning process through gloss annotations, either in an end2end manner or by involving an intermediate step…
Translating away Translationese without Parallel Data
Rricha Jalota, Koel Dutta Chowdhury, Cristina España-Bonet +1
Translated texts exhibit systematic linguistic differences compared to original texts in the same language, and these differences are referred to as translationese. Translationese…
Measuring Spurious Correlation in Classification: 'Clever Hans' in Translationese
Angana Borah, Daria Pylypenko, Cristina Espana-Bonet +1
Recent work has shown evidence of 'Clever Hans' behavior in high-performance neural translationese classifiers, where BERT-based classifiers capitalize on spurious correlations, in…
When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages
Niyati Bafna, Cristina España-Bonet, Josef van Genabith +2
Most existing approaches for unsupervised bilingual lexicon induction (BLI) depend on good quality static or contextual embeddings requiring large monolingual corpora for both lang…
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?
Sonal Sannigrahi, Josef van Genabith, Cristina Espana-Bonet
Dense vector representations for textual data are crucial in modern NLP. Word embeddings and sentence embeddings estimated from raw texts are key in achieving state-of-the-art resu…
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?
Kwabena Amponsah-Kaakyire, Daria Pylypenko, Josef van Genabith +1
Recent work has shown that neural feature- and representation-learning, e.g. BERT, achieves superior performance over traditional manual feature engineering based approaches, with…