Papers by Frank Palma Gomez
Multi-Reference Benchmarks for Russian Grammatical Error Correction (2024.eacl-long)
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| Challenge: | Using the union of the references increases system scores by more than 10 points, but not across error types. |
| Approach: | They propose multi-reference benchmarks for the Grammatical Error Correction of Russian . they use two existing single-refer datasets for a total of 7,444 learner sentences . |
| Outcome: | The proposed benchmarks show that new raters tend to make more changes, especially at the lexical level, compared to the original rater. |
Using Neural Machine Translation for Generating Diverse Challenging Exercises for Language Learner (2023.acl-long)
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| Challenge: | a common challenge for language learners is understanding how to appropriately use words that may have similar meanings but are used in different contexts. |
| Approach: | They propose a method to automatically generate distractors for cloze exercises for English language learners using round-trip neural machine translation. |
| Outcome: | The proposed method generates distractors for cloze exercises for English learners . it shows that the generated distractors are of the same difficulty as human distractors . |
Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation (2022.acl-srw)
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| Challenge: | a fill-in-the-blank exercise involves removing one word from a sentence and generating distractors . a valid distractor is a word that does not fit the context, and distractors are invalid . |
| Approach: | They propose to automatically generate distractors using round-trip neural machine translation . they show that using hundreds of translations for a given sentence generates a rich set of distractors . |
| Outcome: | The proposed method outperforms two strong baselines against a real corpus of cloze exercises and manually checks for validity. |
Low-Resource Grammatical Error Correction: Selective Data Augmentation with Round-Trip Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods for grammatical error correction require large amounts of parallel training data. |
| Approach: | They propose to generate synthetic data through round-trip machine translation by generating a set of character-level errors using a technique known as SeLex-RT. |
| Outcome: | The proposed technique produces errors similar to those observed with language learners, but lacks gold-labeled training data. |