edATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts
This work provides a significant improvement in typing efficiency and accuracy for users of Abugida script languages, addressing a long-standing usability problem for a large population.
This paper addresses the challenges of texting in languages with Abugida scripts, which suffer from large character sets requiring multiple keyboard views or inconsistent romanization. The authors propose a disambiguation algorithm that improves typing speed by 19.49% in Hindi, 25.13% in Bengali, and 14.89% in Thai using ambiguous input, and increases Error Correction F1 score by 10.03% and Next Word Prediction by 62.50% on average for romanized word variant disambiguation.
Abugida refers to a phonogram writing system where each syllable is represented using a single consonant or typographic ligature, along with a default vowel or optional diacritic(s) to denote other vowels. However, texting in these languages has some unique challenges in spite of the advent of devices with soft keyboard supporting custom key layouts. The number of characters in these languages is large enough to require characters to be spread over multiple views in the layout. Having to switch between views many times to type a single word hinders the natural thought process. This prevents popular usage of native keyboard layouts. On the other hand, supporting romanized scripts (native words transcribed using Latin characters) with language model based suggestions is also set back by the lack of uniform romanization rules. To this end, we propose a disambiguation algorithm and showcase its usefulness in two novel mutually non-exclusive input methods for languages natively using the abugida writing system: (a) disambiguation of ambiguous input for abugida scripts, and (b) disambiguation of word variants in romanized scripts. We benchmark these approaches using public datasets, and show an improvement in typing speed by 19.49%, 25.13%, and 14.89%, in Hindi, Bengali, and Thai, respectively, using Ambiguous Input, owing to the human ease of locating keys combined with the efficiency of our inference method. Our Word Variant Disambiguation (WDA) maps valid variants of romanized words, previously treated as Out-of-Vocab, to a vocabulary of 100k words with high accuracy, leading to an increase in Error Correction F1 score by 10.03% and Next Word Prediction (NWP) by 62.50% on average.