Neural-based T amil Grammar Error Detection (2021)

Abstract: This paper describes an ongoing development of a grammar error checker for the Tamil language using a state-of-the-art deep neural-based approach. This proposed checker capture a vital type of grammar error called subject-predicate agreement errors. In this case, we specifically target the agreement error that occurs between nominal subject and verbal predicates. We also created the first-ever grammar error annotated corpus for Tamil. In addition, we experimented with different multi-lingual pre-trained language models to capture syntactic information and found that IndicBERT gives better performance for our tasks. We implemented this grammar checker as a multi-class classification on top of the IndicBERT pre-trained model, which we fine-tuned using our annotated data. This baseline model gives an F1 Score of 73.4. We are now in the process of improving this proposed system with the use of a dependency parser.

## introduction grammar error detection is the task of identifying grammatical errors in the text. this feature is available as a part of stand-alone applications, such as microsoft word, libre office, and online applications, such as grammarly and google docs. however, none of these applications supports the grammar error detection of tamil and most other indian languages. in recent times, neural-based approaches are also being employed for grammar error detection tasks. however, unlike other well-resourced languages such as english and german, applying neural-based approaches to tamil is difficult due to the lack of quality annotated data. this paper outlines how we implemented an application to detect grammar errors related to the subject-predicate agreement in tamil. we have created a grammar error annotated corpus to train the application. we have employed a neural-based approach and a transfer learning technique to implement the proposed application. ## motivation tamil is a morphosyntactically rich and free-order language. it is spoken by more than 78 million people around the world, foot_0 and is the official language of sri lanka, singapore, and tamil nadu, india. tamil is a diglossic language with a spoken and written form. the spoken form varies from region to region; however, the written form is almost the same among regions. tamil documents are being prepared electronically nowadays, including official documents. however, most of the time, these documents are typed in by people who are not well versed with tamil grammar. on the other hand, official government documents are not supposed to have any grammar mistakes. it is even more critical in a multi-lingual country such as sri lanka, where sometimes even a non-tamil person may type in official letters. therefore, all documents have to be checked for all types of errors and corrected. further, nowadays, many efforts are being made to develop machine translation systems. we need to ensure that translations are grammatically correct before using them to train systems. ## literature review several studies have been carried out to develop spell checkers for tamil, including @xcite @xcite . however, no grammar error checkers are found online or integrated into other applications. on the other hand, grammar error checkers for well-resourced languages are readily available online as cloud-based tools such as google docs, grammarly, and stand-alone office suites. there are 28 different types of errors that have been reported in literature @xcite . in addition to the listed errors, tamil also has a special type of error called sandhi error. although sandhi is considered as the result of a phonological operation between two words or two morphs, sandhi also shows syntactic clues as discussed shown by @xcite . in this respect, vaani foot_1 , which is developed using a rulebased approach, can be considered as a partial grammar checker as it handles sandhi errors. nowadays, several multi-lingual pre-trained models @xcite are available online. these models are trained with millions of sentences and tokens. the pre-trained models capture various linguistic information, including morphological, syntactical and semantic information of sentences. however, these details are not in a specific for-mat; therefore, not very easy to retrieve. xlm-r @xcite , indicbert @xcite , and bert @xcite are also trained with tamil data. therefore, these models also capture linguistic features of tamil. we require a large set of annotated corpus to train a machine learner to carry out the task of our interests. however, the tamil language does not have an error annotated corpus. this kind of error annotated corpora can be created not only by hand but also with the assistance of tools like part of speech taggers, morphological analysers, and syntactic parsers. ## the proposed grammar error detector this section outlines the process that has been followed to develop the proposed grammar error detector using a neural-based approach and transferlearning technique. ## approach as illustrated in figure 1 , we used a supervised approach to develop the proposed grammar error detector. however, instead of training a model from scratch, which requires a significant amount of data and processing power, we used a pre-trained language model to capture the morphosyntax and then modelled the grammar error detection as a multi-class classification problem on top of it. in order to do that, we have created a grammar error annotated corpus to fine-tune the pre-trained model and implement our classification model. we used our training set and validation set for this purpose, and then we evaluated the system using the test set. ## conclusion we have implemented a baseline application for tamil grammatical error detection using the stateof-the-art approach. the application outlined here detects grammatical errors related to the personnumber-gender agreement between the nominative subject and the verbal predicate in a sentence. we used a multi-lingual pre-trained model to capture the tamil structures and then fine-tuned it using the grammar error annotated data we created. we found that the indicbert model gives better accuracy than other pre-trained models. our baseline model shows an f1 score of 84.0% for unseen a test set. as the next step, we are planning to use thamizhimorph @xcite -a morphological analyser to create more annotated data to train the grammar checker. the current model relies on the pre-trained model to capture the syntactic information such as subject and predicate. however, this can be obtained using a syntactic parser, and the syntactically parsed data may increase the score. therefore, as the next step, we will also experiment with a tamil dependency parser called thamizhiudp @xcite to incorporate syntactic information such as subject and predicate information into our datasets to see whether the proposed system can be improved further.