9 research outputs found
Temporality as seen through translation: a case study on Hindi texts
Temporality has significantly contributed to various aspects of Natural Language
Processing applications. In this paper, we determine the extent to which temporal
orientation is preserved when a sentence is translated manually and automatically
from the Hindi language to the English language. We show that the manually and
automatically identified temporal orientation in English translated (both manual
and automatic) sentences provides a good match with the temporal orientation of
the Hindi texts. We also find that the task of manual temporal annotation becomes
difficult in the translated texts while the automatic temporal processing system manages to correctly capture temporal information from the translations
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages
Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality