Computational Challenges for Polysynthetic Languages
Given advances in computational linguistic analysis of complex languages using Machine Learning as well as standard Finite State Transducers, coupled with recent efforts in language revitalization, the time was right to organize a first workshop to bring together experts in language technology and linguists on the one hand with language practitioners and revitalization experts on the other. This one-day meeting provides a promising forum to discuss new research on polysynthetic languages in combination with the needs of linguistic communities where such languages are written and spoken.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
Challenges in Speech Recognition and Translation of High-Value Low-Density Polysynthetic Languages
Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages
Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme complexity and to the word class {``}squish{''}. In addition, many of these polysynthetic languages are low-resource. We propos…
Morphological AnalysisBPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages
Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation. We investigate a wide variety of supervi…
Machine TranslationSegmentationTranslationLost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translation performance is far from the state of …
Machine TranslationTranslation