Neural Sequence-to-sequence Learning of Internal Word Structure
Tatyana Ruzsics, Tanja Samardżić · 2017
Learning internal word structure has recently been recognized as an important step in various multilingual processing tasks and in theoretical language comparison.In this paper, we present a neural encoder-decoder model for learning canonical morphological segmentation.Our model combines character-level sequence-to-sequence transformation with a language model over canonical segments.We obtain up to 4% improvement over a strong character-level encoderdecoder baseline for three languages.Our model outperforms the previous state-ofthe-art for two languages, while eliminating the need for external resources such as large dictionaries.Finally, by comparing the performance of encoder-decoder and classical statistical machine translation systems trained with and without corpus counts, we show that including corpus counts is beneficial to both approaches.