Papers by Steve DeNeefe
Domain adapted machine translation: What does catastrophic forgetting forget and why? (2024.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) models can be specialized by domain adaptation, often fine-tuning on a dataset of interest. |
| Approach: | They propose a novel approach to understanding catastrophic forgetting during NMT adaptation by investigating the relationship between the data and the in-domain vocabulary coverage. |
| Outcome: | The proposed model can be specialized by fine-tuning on a domain of interest, but can fail to achieve the predicted quality of the target domain. |
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents (2021.eacl-demos)
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| Challenge: | Existing systems that generate and answer questions in a question-and-answer format can facilitate reading comprehension. |
| Approach: | They propose a system that integrates question answering and question generation tasks to produce a list of Q&A items for a text. |
| Outcome: | The proposed system generates a catalog of Q&A items for a text. |
AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature (2023.eacl-main)
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| Challenge: | Creating an abridged version of a text requires shortening it while maintaining its linguistic qualities. |
| Approach: | They propose an abridgement task that requires shortening and maintaining linguistic qualities of a text while maintaining its linguistic quality. |
| Outcome: | The proposed dataset captures passage-level alignments between original and abridged texts . it can be used to generate a bridge and shorten the original text . |