Leveraging Alignment and Phonology for low-resource Indic to E nglish Neural Machine Transliteration (2020)

Abstract: In this paper we present a novel transliteration technique based on Orthographic Syllable(OS) segmentation for low-resource Indian languages (ILs). Given that alignment has produced promising results in Statistical Machine Transliteration systems and phonology plays an important role in transliteration, we introduce a new model which uses alignment representation similar to that of IBM model 3 to pre-process the tokenized input sequence and then use pre-trained source and target OS-embeddings for training. We apply our model for transliteration from ILs to English and report our accuracy based on Top-1 Exact Match. We also compare our accuracy with a previously proposed Phrase-Based model and report improvements.

## introduction the process of transliteration is defined in @xcite as "the conversion of a given name in the source language (a text string in source script) to a name in the target language (another text string in target script), such that the target language name is: (i) phonemically equivalent to the source name, (ii) conforms to the phonology of the target language, and (iii) matches the user intuition of the equivalent of the source language name in the target language, considering the culture and orthographic character usage in the target language". this definition of transliteration is apt in the context of machine translation since it employs transliteration as a subsystem to handle named entities (nes). we are interested in solving the transliteration problem for indian to english language pairs and in this paper, we demonstrate the use of os and pre-trained embeddings to overcome the data sparsity problem that arises in lowresource languages. the structure of the paper is as follows: section 2 presents the state of the art on machine transliteration. in section 3 and 4, we describe some background and our proposed approach. then, in section 5, we present our experiments and results. finally, in section 6, we present our conclusions and in section 7, we express gratitude to our supporters. ## background knowledge we first describe the orthographic syllables, which form the essence of this transliteration module, introducing a new technique for word segmentation, following which we will formulate the probabilistic model for grapheme-to-grapheme alignment, explaining the method to position each orthographic syllable in the word. ## approach in this section, we present our approach, followed by a description of our experimental setup, describing the data gathering and cleaning, followed by the model configurations and then the evaluation technique. ## results we discuss and analyse the results of our experiments, indicating the major improvements and scope of improvement for our approach. ## conclusion and future work we show that using pre-trained osembeddings on neural encoder-decoder architecture involving os tokenization outperforms the baseline system by a significant margin. the results also support our claim that phonology and alignment play an important role in increasing the accuracy of transliteration. the reason for the improvement could be learning the akshar (a combination of vowel and consonant) representation by encoder network and the ability to learn canonical spellings in english. given the benefits of using alignment and os embeddings for low-resource ils, we intend to explore il to il transliteration with and without english as a pivot.