A deep learning approach for Malayalam morphological analysis at character level

A Morphological Processor for Malayalam Language

Work on morphological analyzers (which are computer programmes) for Indian languages is conducted vigorously these days. Usually published in specialized journals, this rather technical work is briefly presented here to provide some insights to a wider readership into little-known aspects of current language work. The morphological strength of Malayalam as a major South Indian language justifie...

Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences

This paper presents the systems submitted by the Abu-MaTran project to the Englishto-Finnish language pair at the WMT 2016 news translation task. We applied morphological segmentation and deep learning in order to address (i) the data scarcity problem caused by the lack of in-domain parallel data in the constrained task and (ii) the complex morphology of Finnish. We submitted a neural machine t...

Deep Character-level Neural Machine Translation by Learning Morphology

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems. However, the use of large vocabulary becomes the bottleneck in both training and improving the performan...

Chinese Morphological Analysis with Character-level POS Tagging

The focus of recent studies on Chinese word segmentation, part-of-speech (POS) tagging and parsing has been shifting from words to characters. However, existing methods have not yet fully utilized the potentials of Chinese characters. In this paper, we investigate the usefulness of character-level part-of-speech in the task of Chinese morphological analysis. We propose the first tagset designed...

Implementation of Malayalam Morphological Analyzer Based on Hybrid Approach