Papers by Cristina España-Bonet
Elote, Choclo and Mazorca: on the Varieties of Spanish (2024.naacl-long)
Copied to clipboard
| Challenge: | Spanish is the official language in 20 countries and the second most-spoken native language . available corpora treat it as one monolithic language, damping prediction power . |
| Approach: | They compile and curate datasets in different varieties of Spanish around the world at an unprecedented scale and create the CEREAL corpus. |
| Outcome: | The results show that Spanish is a multilingual language with a wide range of cultural and cultural influences. |
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Several platforms currently rate news outlets according to their political bias, but they are not ready for use in the near future. |
| Approach: | They use ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations along with automatically extracted topic annotations. |
| Outcome: | The proposed model can identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. |
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on curriculum learning focus on selecting the best distribution of data to train a system. |
| Approach: | They propose a self-supervised neural machine translation model that self-selects data without being told to do so. |
| Outcome: | The proposed model self-selects samples of increasing complexity and task relevance without being told to do so, and performs a denoising curriculum. |
Self-Supervised Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) methods relied on the availability of high-quality parallel corpora. |
| Approach: | They propose a method where an emergent NMT system is used for selecting training data and learning internal NMT representations. |
| Outcome: | The proposed method achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training. |
Analysing Coreference in Transformer Outputs (D19-65)
Copied to clipboard
| Challenge: | Using a transformer architecture, we study coreference phenomena in three neural machine translation systems. |
| Approach: | They analyse coreference phenomena in three neural machine translation systems . they manually annotate (the possibly incorrect) coreference chains in the outputs . |
| Outcome: | The proposed model shows stronger translationese effects in machine translated outputs than in human translations. |
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification (2021.emnlp-main)
Copied to clipboard
Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury, Josef van Genabith, Cristina España-Bonet
| Challenge: | Traditional hand-crafted features have been used for distinguishing between translated and original non-translated texts. |
| Approach: | They compare a feature-engineering-based approach to a features-learning-based one and use pre-trained neural word embeddings to train neural architectures. |
| Outcome: | The proposed approach outperforms other approaches by more than 20 accuracy points and the BERT-based model performs the best in both monolingual and multilingual settings. |
Context-Aware Neural Machine Translation Decoding (D19-65)
Copied to clipboard
| Challenge: | Existing approaches to enhance neural machine translation systems to take into account document-level information make the training process slower or require document- level annotated data. |
| Approach: | They propose a decoding architecture that fuses the semantic space language model and a neural translation model. |
| Outcome: | The proposed approach improves translation quality for English–Spanish using BLEU and METEOR. |
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (2020.lrec-1)
Copied to clipboard
| Challenge: | a recent study shows that word embeddings can be useful for training downstream natural language processing tasks. |
| Approach: | They compare word embeddings obtained by word embeds from curated corpora with a language-dependent processing. |
| Outcome: | The proposed model compares word embeddings with word embeds from curated corpora and a language-dependent processing on two African languages. |
AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
Copied to clipboard
Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
| Challenge: | AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs . |
| Approach: | They propose a document-level multi-parallel translation dataset covering English and five African languages. |
| Outcome: | The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models . |
Towards Debiasing Translation Artifacts (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies show translation artifacts in translations influence performance of cross-lingual tasks. |
| Approach: | They propose a method to reduce translation artifacts by extending an established bias-removal technique. |
| Outcome: | The proposed method reduces translationese at sentence and word level . it is the first study to debias translations on a natural language inference task . |
The (Undesired) Attenuation of Human Biases by Multilinguality (2022.emnlp-main)
Copied to clipboard
| Challenge: | odor pleasantness perception is universal, but cultural biases are not always present in embedding models . et al., 2018: a new study shows that cultural bias is not always the case in embedded models based on human texts . |
| Approach: | They propose multilingual cultural aware tests to quantify biases in embedding models . they find that biased models are more likely to be multilingual than monolingual ones . |
| Outcome: | The results show that human preferences are not always universal . they also show that multilinguality reverses biases, despite differences in training corpus . |
GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies (2020.lrec-1)
Copied to clipboard
| Challenge: | a tool for extracting multilingual parallel corpora at sentence level with document and gender information from Wikipedia biographies. |
| Approach: | They propose a tool for extracting multilingual parallel corpora at sentence level with document and gender information from Wikipedia biographies. |
| Outcome: | The proposed tool extracts a corpus of 2,000 sentences in English, Spanish and Catalan. |
Translating away Translationese without Parallel Data (2023.emnlp-main)
Copied to clipboard
| Challenge: | Translated texts exhibit systematic linguistic differences compared to original texts in the same language, referred to as translationese . studies show translationeses have effects on various cross-lingual natural language processing tasks . |
| Approach: | They propose a translation-based style transfer approach that learns from monolingual data . they combine a self-supervised approach with an unsupervised approach . |
| Outcome: | The proposed method reduces translationese to a level of a random classifier after style transfer while preserving the content and fluency in the target original style. |
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
Copied to clipboard
| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
| Approach: | They compare methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. |
| Outcome: | The proposed methods produce document-level representations from sentences in 8 languages . the results show that a clever combination of sentence embeddings is usually better than encoding the full document as a single unit. |
Understanding Translationese in Multi-view Embedding Spaces (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies show footprints of the source language remain visible in translations . this is the first time departures from isomorphism between embedding spaces are used to track translationese. |
| Approach: | They exploit departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. |
| Outcome: | The proposed method exploits departures from isomorphism between embedding spaces to predict relations between languages in an unsupervised way. |
When Your Cousin Has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for unsupervised bilingual lexicon induction depend on good quality static or contextual embeddings for both languages. |
| Approach: | They propose a method for unsupervised bilingual lexicon induction between a related LRL and a high-resource language that only requires inference on a masked language model of the HRL. |
| Outcome: | The proposed method performs well on low-resource languages with 5M tokens against Hindi . it is compared with existing methods on (mid-resourced) Marathi and Nepali . |
DGS-Fabeln-1: A Multi-Angle Parallel Corpus of Fairy Tales between German Sign Language and German Text (2024.lrec-main)
Copied to clipboard
Fabrizio Nunnari, Eleftherios Avramidis, Cristina España-Bonet, Marco González, Anna Hennes, Patrick Gebhard
| Challenge: | a parallel corpus of German text and videos containing fairy tales interpreted into the German Sign Language (DGS) is the first corpus filmed from 7 angles and one of the few sign language corpora globally which have been filmed simultaneously. |
| Approach: | They present a parallel corpus of German fairy tales interpreted by a native DGS signer. |
| Outcome: | The proposed corpus is the first semi-naturally expressed DGS that has been filmed from 7 angles and where the listener has been simultaneously filmed. |
Continual Learning in Multilingual Sign Language Translation (2025.naacl-long)
Copied to clipboard
| Challenge: | Despite the low translation quality of sign language, many machine learning approaches are still in its infancy. |
| Approach: | They propose to use continual learning for mul- tilingual SLT to improve translation quality. |
| Outcome: | The proposed methods outperform baseline and fine-tuning approaches in sign language translation. |
Sign Language Translation with Sentence Embedding Supervision (2024.acl-short)
Copied to clipboard
| Challenge: | State-of-the-art sign language translation systems facilitate learning through gloss annotations when available at scale. |
| Approach: | They propose to use sentence embeddings of the target sentences at training time that take the role of glosses to supervise the learning process. |
| Outcome: | The proposed method significantly outperforms gloss-free approaches on German and American sign languages and with mono- and multilingual sentence embeddings and translation systems. |