Papers by Peter Makarov
Semi-supervised Contextual Historical Text Normalization (2020.acl-main)
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| Challenge: | Historical text normalization is the task of mapping historical word forms to their modern counterparts. |
| Approach: | They propose to use a generative normalization model to obtain contextualization from the target-side language model. |
| Outcome: | et al., 2018) show that the most effective approach reduces manual normalization time and manual training costs. |
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
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| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
Imitation Learning for Neural Morphological String Transduction (D18-1)
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| Challenge: | Recent studies have shown that neural transition-based models can be used for morphological tasks such as inflection generation and lemmatization without a character aligner or warm start. |
| Approach: | They propose to use imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. |
| Outcome: | The proposed model eliminates the need for a character aligner or warm start and achieves state-of-the-art performance on several datasets. |