A huge amount of valuable resources is available on the web in English, which are often translated into local languages to facilitate knowledge sharing among local people who are not much familiar with English. However, translating such content manually is very tedious, costly, and time-consuming process. To this end, machine translation is an efficient approach to translate text without any human involvement. Neural machine translation (NMT) is one of the most recent and effective translation technique amongst all existing machine translation systems. In this paper, we apply NMT for English-Tamil language pair. We propose a novel neural machine translation technique using word-embedding along with Byte-Pair-Encoding (BPE) to develop an efficient translation system that overcomes the OOV (Out Of Vocabulary) problem for languages which do not have much translations available online. We use the BLEU score for evaluating the system performance. Experimental results confirm that our proposed MIDAS translator (8.33 BLEU score) outperforms Google translator (3.75 BLEU score).

Despite the recent advancements of attentionbased deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a lowresource setting because of a lack of pretrained models for such languages. In this study, we make the first attempt to investigate the challenges of adapting these techniques to an extremely low-resource language -Sumerian cuneiform -one of the world's oldest written language attested from at least the beginning of the 3rd millennium BC. Specifically, we introduce the first cross-lingual information extraction pipeline for Sumerian, which includes part-of-speech tagging, named entity recognition, and machine translation. We introduce InterpretLR, an interpretability toolkit for low-resource NLP and use it alongside human evaluations to gauge the trained models. Notably, all our techniques and most components of our pipeline can be generalised to any low-resource language. We publicly release all our implementations including a novel data set with domain-specific pre-processing to promote further research in this domain.2. 1(disz) kusz masz2 niga 1 hide, grain-fed goat;3. kusz udu sa2-du11 sheep hides, regular offerings, 4. ki {d}iszkur-illat-ta from Adda-illat, obverse.reverse.

No abstract

No abstract

On the lines of the huge and varied efforts in the field of automation with respect to technology development and innovation of vehicles to make them run on electric power and moreover autonomously, this paper presents an innovation to a bicycle. A normal daily-use bicycle was modified at low-cost such that it runs autonomously, while maintaining its original form i.e. the manual drive. Hence, a bicycle which could be normally driven by any human and with a press of switch could run autonomously according to the users needs has been developed.