Apertium-kan-mar
Contents
Description
The goal of this project was to develop a rule-based translation system for Kannada-Marathi pair for Apertium.
Kannada
The Kannada monolingual dictionary was developed from scratch as there was no pre-existing work based on Kannada from Apertium. The Kannada-Marathi bilingual dictionary was also developed from scratch with the help of Marathi monoligual dictionary.
My commits can be accessed at the following link: commits These are the dependent GitHub repositories of my GSoC 2018 project. My GitHub account name is MissingBytes
| Github repositories |
|---|
| Kannada monolingual dictionary |
| Kannada-Marathi bilingual dictionary |
| Marathi monolingual dictionary |
I worked on the Kannada monolingual dictionary and Kannada-Marathi bilingual dictionary from scratch. I haven't made any changes in the Marathi monolingual dictionary.
Summary
Apertium particularly shines when used for languages with similar grammatical structures, and Romance and Turkic languages have been a very active area for language pair developers. Turkish and Crimean Tatar, though from different branches of the Turkic family (Oghuz and Kipchak respectively), have many similarities in phonetics, morphology and even syntax mostly due to Ottoman influence on the Crimean Tatar language.
Coverage
Coverage is the percentage of words the translation system could analyse(or assign parts of speech tag-MonoDix/map words-BiDix) in a given text. For a translation system, it is necessary to do the morphological analysis using the dictionaries. The morphological analysis of Kannada was difficult due to high agglutinativity and morphological constraints. For By coverage we mean the amount of the input text that the system understands and attempts to analyze and translate into the target language. This is an important metric and is related to the presence of the necessary words and morphology in the dictionaries. The system required the development of a Crimean Tatar-Turkish lexicon. The lack of a Turkish-Crimean Tatar dictionary was one obstacle in the path of the project. We used resources such as Wiktionary and crossed a Russian-Qırımtatar dictionary [1] with a Russian-Turkish one to create an initial bilingual dictionary. After that more words were added through cognates through Turkish, corpora were examined to determine and ascertain unknown words' meanings and Persian, Arabic and Russian vocabulary were used to good effect to reach a high coverage on all the corpora.
| Corpus | Coverage |
|---|---|
| WikiMedia Corpus | 85.70% |
| cuni | 78.94% |
Analyzers for both Crimean Tatar and Turkish were available in Apertium. Any entry in the bilingual dictionary (bidix) missing from either analyzer was added to the analyzers as well.
Transfer
There are 51 structural transfer rules that take Crimean Tatar constructions and turn them into their Turkish equivalents. Many of these rules cover constructions that are analytic in Qırımtatar and synthetic in Turkish, the most simple examples being things like yapa bile, "he/she/it can do it" which would translate to yapabilir. Copulae like edi, eken, ekende are also often written together with the verb in Turkish as opposed to their Crimean Tatar counterparts.
Disambiguation and Lexical Selection
Disambiguation
Many different analyses are generated for many word forms. To correctly discern the lemma and the morphology so as to be translated correctly into the target language, MT systems have disambiguation components. The disambiguation in this system is currently carried out using Constraint Grammar (CG). 68 rules remove the wrong analyses and select the the correct ones with the use of contextual morphological information. Ideally this would be either in conjunction with or replaced by a machine-learned POS tagger, which requires a tagged corpus. The tagged corpus will be developed in the near future.
Lexical Selection
Lexical selection is used when the system needs to choose among multiple possible translations. The lexical selection component uses rules to choose which translation to prefer based on contextual information.
