Papers by Maria Nadejde
Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1 (D19-55)
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| Challenge: | Grammar error correction systems have become ubiquitous in a variety of software applications, but little is known about how to efficiently personalize them to the user’s characteristics, such as proficiency level and first language. |
| Approach: | They propose to adapt a general purpose neural GEC system to the proficiency level and the first language of a writer, using only a few thousand annotated sentences. |
| Outcome: | The proposed system improves on adapting to proficiency level and first language . the results are the broadest of its kind, covering five proficiency levels and twelve different languages. |
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation (2023.acl-short)
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| Challenge: | Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of outputs. |
| Approach: | They propose a new approach to attribute-controlled translation that leverages multilingual language models to perform ACT in few-shot and zero-shot settings. |
| Outcome: | The proposed approach improves generation accuracy over the standard prompting approach in both zero-shot and few-shot settings. |
CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality (2022.findings-naacl)
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| Challenge: | Specific problems arise when translating from English into languages with formality markers, such as “Are you sure?” . Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. |
| Approach: | They propose to train formality-controlled models by fine-tuning on labeled contrastive data and a metric to evaluate them. |
| Outcome: | The proposed model achieves high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality. |
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation (2022.emnlp-main)
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Anna Currey, Maria Nadejde, Raghavendra Reddy Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu
| Challenge: | Existing benchmarks have limited diversity in terms of gender phenomena, sentence structure, or language coverage. |
| Approach: | They propose a benchmark to evaluate gender accuracy in translation from English into eight widely-spoken languages. |
| Outcome: | The proposed benchmark provides realistic, gender-balanced, counterfactual data in eight language pairs where the gender of individuals is unambiguous in the input segment. |
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation (2024.naacl-short)
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Benjamin Hsu, Xiaoyu Liu, Huayang Li, Yoshinari Fujinuma, Maria Nadejde, Xing Niu, Ron Litman, Yair Kittenplon, Raghavendra Pappagari
| Challenge: | Document translation is a challenge for machine translation systems that focus on textual content at the sentence level, ignoring global context and visual layout structure. |
| Approach: | They propose a benchmark dataset to evaluate document-level NMT systems . they use visual cues to preserve reading order and contiguous blocks of text . |
| Outcome: | The proposed benchmarks assess document-level NMT systems on the comprehensive task of translating semi-structured documents. |