Papers by Moussa Kamal Eddine
DATScore: Evaluating Translation with Data Augmented Translations (2023.findings-eacl)
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| Challenge: | Experimental results show that DATScore correlates better with human meta-evaluations than the other recent state-of-the-art metrics. |
| Approach: | They propose to use data augmented translations to improve the evaluation of machine translations by using two new scoring strategies. |
| Outcome: | The proposed metric improves on 3 NLG tasks other than translation. |
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model (2021.emnlp-main)
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| Challenge: | Inductive transfer learning has taken the entire NLU field by storm, with models such as BERT and BART setting new state-of-the-art on countless tasks. |
| Approach: | They introduce a large-scale pretrained seq2seq model for French that is very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. |
| Outcome: | The proposed model outperforms existing models on discriminative and generative tasks on a French summarization dataset. |
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation (2022.acl-long)
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| Challenge: | Existing evaluation metrics are not reliable, but require significant computational resources. |
| Approach: | They propose a method to learn a fixed, low cost version of any expensive NLG metric while retaining most of its original performance. |
| Outcome: | The proposed approach retains most of the original performance while running faster and faster. |
Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency (2022.emnlp-main)
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| Challenge: | Abstractive summarization systems have a lack of a defined definition for the task . factual consistency is a key factor in summarizing, but there are still deficiencies . a new study shows that summarized summarisation models achieve improved performance . |
| Approach: | They propose a filtered summarization dataset with improved factual consistency to address this problem . they argue that the dataset should become a valid benchmark for developing and evaluating summarizing systems . |
| Outcome: | The proposed model improves on a popular summarization dataset with improved factual consistency. |