Morphology & word sense disambiguation embedded multimodal neural machine translation system between Sanskrit and Malayalam

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Machine translation is the process of conversion from one language to another using computers. In spite of the existence of several traditional machine translation systems like rule-based, statistical etc., the development of neural machine translation has significantly improved translation quality. However, the morphological richness and agglutinative nature of Indian languages hampers the usage of neural machine translation for translation among Indian languages despite its potential to overcome the difficulties posed by conventional translation systems. Hence the combination of neural machine translation with morphology, part of speech tagger and word sense disambiguation is proposed to address these challenges and adapt it for bidirectional translation between Sanskrit and Malayalam. Conventional neural machine translations are unimodal systems that utilize textual data for translation. Translation quality of conventional neural machine translation is enhanced with a novel multimodal bidirectional neural machine translation system with text & speech as the two modalities based on parallel text corpus and speech database. The best performance score is obtained for two level fusion, the feature vectors extracted from speech signals using different transforms (Wavelet transform, 1-D sequency mapped real transform & 1-D GCD based mapped real transform) are fused and which is further fused with a context vector from the text modality. Automatic and manual review methods are employed to assess quality of translation. Findings from the experiments reveal that addition of speech modality improved the overall quality of translation. BLEU scores of 43.89 and 42.72 are obtained for Sanskrit to Malayalam and Malayalam to Sanskrit multimodal translation respectively.