Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages

Under-resourced languages are a significant challenge for statistical approaches to machine translation, and recently it has been shown that the usage of training data from closely-related languages can improve machine translation quality of these languages. While languages within the same language family share many properties, many under-resourced languages are written in their own native script, which makes taking advantage of these language similarities difficult. In this paper, we propose to alleviate the problem of different scripts by transcribing the native script into common representation i.e. the Latin script or the International Phonetic Alphabet (IPA). In particular, we compare the difference between coarse-grained transliteration to the Latin script and fine-grained IPA transliteration. We performed experiments on the language pairs English-Tamil, English-Telugu, and English-Kannada translation task. Our results show improvements in terms of the BLEU, METEOR and chrF scores from transliteration and we find that the transliteration into the Latin script outperforms the fine-grained IPA transcription

Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark

Multimodal Hate Speech Detection from Bengali Memes and Texts

Numerous works have been proposed to employ machine learning (ML) and deep learning (DL) techniques to utilize textual data from social media for anti-social behavior analysis such as cyberbullying, fake news propagation, and hate speech mainly for highly resourced languages like English. However, despite having a lot of diversity and millions of native speakers, some languages such as Bengali are under-resourced, which is due to a lack of computational resources for natural language processing (NLP). Like English, Bengali social media content also includes images along with texts (e.g., multimodal contents are posted by embedding short texts into images on Facebook), only the textual data is not enough to judge them (e.g., to determine they are hate speech). In those cases, images might give extra context to properly judge. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech detection dataset1 for a kind of problem for Bengali. We train several neural architectures (i.e., neural networks like Bi-LSTM/Conv-LSTM with word embeddings, EfficientNet + transformer architectures such as monolingual Bangla BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) jointly analyze textual and visual information for hate speech detection. The Conv-LSTM and XLM-RoBERTa models performed best for texts, yielding F1 scores of 0.78 and 0.82, respectively. As of memes, ResNet152 and DenseNet201 models yield F1 scores of 0.78 and 0.7, respectively. The multimodal fusion of mBERT-uncased + EfficientNet-B1 performed the best, yielding an F1 score of 0.80. Our study suggests that memes are moderately useful for hate speech detection in Bengali, but none of the multimodal models outperform unimodal models analyzing only textual data

A Sentiment Analysis Dataset for Code-Mixed Malayalam-English

There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff's alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts

DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7,000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff's alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning methods. The dataset is available on Github (https://github.com/bharathichezhiyan/DravidianCodeMix-Dataset) and Zenodo (https://zenodo.org/record/4750858\#.YJtw0SYo\_0M).Comment: 36 page