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 . |
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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 . |
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| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
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| Challenge: | Existing multilingual machine translation approaches focus on English-centric directions, while non-English directions lag behind. |
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Mixup Decoding for Diverse Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing methods for generating multiple translations for source and target languages neglect the one-to-many mapping between the source and the target languages. |
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Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
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Grammatical Error Correction through Round-Trip Machine Translation (2023.findings-eacl)
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Sparse and Constrained Attention for Neural Machine Translation (P18-2)
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| Challenge: | Existing approaches to address coverage problem only change attention transformations . adequacy of neural machine translation is still a major concern . |
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