Papers by Janet Wiles
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation (2022.lrec-1)
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| Challenge: | Transfer learning is a promising direction for low-resource neural machine translation (NMT) but it introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. |
| Approach: | They conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. |
| Outcome: | The results suggest that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural machine translation. |