Papers by Abteen Ebrahimi
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages (2022.acl-long)
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Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann
| Challenge: | Pretrained multilingual models can perform cross-lingual transfer in a zero-shot setting, even for unseen languages. |
| Approach: | They propose to extend XNLI to 10 indigenous languages of the Americas and test multiple zero-shot and translation-based approaches. |
| Outcome: | The proposed model can perform cross-lingual transfer in a zero-shot setting even for languages unseen during pretraining. |
Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer (2025.emnlp-main)
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| Challenge: | NN-Rank is an algorithm for ranking source languages for cross-lingual transfer . it leverages hidden representations from multilingual models and unlabeled target-language data . |
| Approach: | They propose an algorithm for ranking source languages for cross-lingual transfer which leverages hidden representations from multilingual models and unlabeled target-language data. |
| Outcome: | The proposed algorithm outperforms state-of-the-art models on in-domain data and shows that it can achieve 92.8% of the NDCG achieved using all available target data. |
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)
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| Challenge: | Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates. |
| Approach: | They propose a method for generating a corpus of parallel meaning representations with rich style markup using freely available and naturally descriptive user reviews. |
| Outcome: | The proposed method can be scalably reused to generate NLG datasets for other domains. |
Zero-Shot vs. Translation-Based Cross-Lingual Transfer: The Case of Lexical Gaps (2024.naacl-short)
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| Challenge: | lexical gaps exist in a variety of domains, such as QA, but they can only be expressed as a combination of words in another language. |
| Approach: | They compare the current performance and long-term viability of two approaches to cross-lingual transfer . they leverage lexical gaps to create a multilingual question answering dataset . |
| Outcome: | The proposed model outperforms zero-shot transfer and machine translation (MT) lexical gaps exist in a variety of domains, including linguistics, linguistic coding, and linguistic analysis. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
| Challenge: | Large multilingual models have inspired a new class of word alignment methods, which work well for pretraining languages. |
| Approach: | They propose to use transformer-based word alignment methods to extract alignments from massive pretrained models. |
| Outcome: | The proposed methods outperform traditional methods for languages unseen to pretraining models, and are competitive with each other. |
How to Adapt Your Pretrained Multilingual Model to 1600 Languages (2021.acl-long)
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| Challenge: | Pretrained multilingual models perform best for languages seen during pretraining . methods exist to improve performance for unseen languages, but have been evaluated using amounts of raw text only available for a small fraction of the world’s languages. |
| Approach: | They evaluate the performance of existing methods to adapt pretrained multilingual models to new languages using a resource available for close to 1600 languages: the New Testament. |
| Outcome: | The proposed models perform best for languages seen during pretraining . the results show that the most efficient approach is simplest and the most accurate . |