Papers by Pamela Shapiro
Curriculum Learning for Domain Adaptation in Neural Machine Translation (N19-1)
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| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
Hard Non-Monotonic Attention for Character-Level Transduction (D18-1)
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| Challenge: | Character-level string-to-string transductions are an important component of NLP tasks . hard non-monotonic attention models have been used for sequence modeling tasks involving characters . |
| Approach: | They propose an exact algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings. |
| Outcome: | The proposed algorithm outperforms soft attention and improves performance over stochastic approximation. |