Papers by Ximena Gutierrez-Vasques
From characters to words: the turning point of BPE merges (2021.eacl-main)
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| Challenge: | morphological complexity is still a major challenge for NLP and the study of language. |
| Approach: | They perform a cross-linguistic comparison following incremental merges of BPE for 47 diverse languages. |
| Outcome: | The results show that language distributions are similar under specific levels of tokenization. |
TeDDi Sample: Text Data Diversity Sample for Language Comparison and Multilingual NLP (2022.lrec-1)
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| Challenge: | a deep understanding of language is being achieved through increased access to data from minority and low-resource languages. |
| Approach: | They present a diversity sample of text data for language comparison and multilingual natural language processing. |
| Outcome: | The TeDDi sample features 89 languages based on the typological diversity sample in the World Atlas of Language Structures . |
Challenges of language technologies for the indigenous languages of the Americas (C18-1)
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| Challenge: | Indigenous languages of the American continent are highly diverse, but have received little attention from the technological perspective. |
| Approach: | They review the research, the digital resources and the available NLP systems for indigenous languages of the American continent . they stress the need of developing language resources and NLP tools for these languages . |
| Outcome: | The authors review the research and the available NLP systems on indigenous languages of the Americas . they argue that the lack of resources and tools can have a negative impact on the communities which depend on these languages . |
Interpretability for Morphological Inflection: from Character-level Predictions to Subword-level Rules (2021.eacl-main)
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| Challenge: | Neural models for morphological inflection have recently attained very high results, but their interpretation remains challenging. |
| Approach: | They propose a linguistically-motivated variant to the encoder-decoder model with attention that incorporates a character-level cross-attention mechanism and a self-attention module over substrings of the input. |
| Outcome: | The proposed model performs well on three typologically-different languages and is highly interpretable. |