India, a country with a large population, possesses two official and twenty-two scheduled languages, making it the most linguistically diverse nation. Despite being one of the scheduled languages, Santali remains a low-resource language. Although Ol Chiki is recognized as the official script for Santali, many continue to use Bengali, Devanagari, Odia, and Roman scripts. In tribute to the upcoming centennial anniversary of the Ol Chiki script, we present an Automatic Speech Recognition for Santali in the Ol Chiki script. Our approach involves cross-lingual transfer learning by utilizing the Whisper framework pre-trained in Bengali and Hindi on the Santali language, using Ol Chiki script transcriptions. With the adoption of the Bengali pre-trained framework, we achieved a Word Error Rate (WER) score of 28.47%, whereas the adaptation of the Hindi pre-trained framework resulted in a score of 34.50% WER. These outcomes were obtained using the Whisper Small framework.

In multilingual nations like India, access to legal information is often hindered by language barriers, as much of the legal and judicial documentation remains in English. Legal Machine Translation (L-MT) offers a scalable solution to this challenge by enabling accurate and accessible translations of legal documents. This paper presents our work for the JUST-NLP 2025 Legal MT shared task, focusing on English–Hindi translation using Transformer-based approaches. We experiment with 2 complementary strategies, fine-tuning a pre-trained OPUS-MT model for domain-specific adaptation and training a Transformer model from scratch using the provided legal corpus. Performance is evaluated using standard MT metrics, including SacreBLEU, chrF++, TER, ROUGE, BERTScore, METEOR, and COMET. Our fine-tuned OPUS-MT model achieves a SacreBLEU score of 46.03, significantly outperforming both baseline and from-scratch models. The results highlight the effectiveness of domain adaptation in enhancing translation quality and demonstrate the potential of L-MT systems to improve access to justice and legal transparency in multilingual contexts.

With the recent surge and exponential growth of social media usage, scrutinizing social media content for the presence of any hateful content is of utmost importance. Researchers have been diligently working since the past decade on distinguishing between content that promotes hatred and content that does not. Traditionally, the main focus has been on analyzing textual content. However, recent research attempts have also commenced into the identification of audio-based content. Nevertheless, studies have shown that relying solely on audio or text-based content may be ineffective, as recent upsurge indicates that individuals often employ sarcasm in their speech and writing. To overcome these challenges, we present an approach to identify whether a speech promotes hate or not utilizing both audio and textual representations. Our methodology is based on the Transformer framework that incorporates both audio and text sampling, accompanied by our very own layer called “Attentive Fusion”. The results of our study surpassed previous stateof-the-art techniques, achieving an impressive macro F1 score of 0.927 on the Test Set.

Even though, machine translation has seen huge improvements in the the last decade, translation quality for Indic languages is still underwhelming, which is attributed to the small amount of parallel data available. In this paper, we present our approach to mitigate the issue of the low amount of parallel training data availability for Indic languages, especially for the language pair English-Manipuri and Assamese-English. Our primary submission for the Manipuri-to-English translation task provided the best scoring system for this language direction. We describe about the systems we built in detail and our findings in the process.

Offensive language identification has been an active area of research in natural language processing. With the emergence of multiple social media platforms offensive language identification has emerged as a need of the hour. Traditional offensive language identification models fail to deliver acceptable results as social media contents are largely in multilingual and are code-mixed in nature. This paper tries to resolve this problem by using IndicBERT and BERT architectures, to facilitate identification of offensive languages for Kannada-English, Malayalam-English, and Tamil-English code-mixed language pairs extracted from social media. The presented approach when evaluated on the test corpus provided precision, recall, and F1 score for language pair Kannada-English as 0.62, 0.71, and 0.66, respectively, for language pair Malayalam-English as 0.77, 0.43, and 0.53, respectively, and for Tamil-English as 0.71, 0.74, and 0.72, respectively.