Abstract

Music has long served as a vehicle for political expression, with protest songs playing a central role in articulating dissent and mobilizing collective action. Yet, despite their cultural significance, the linguistic and acoustic signatures that define protest music remain understudied. We present a multimodal computational analysis of protest and non-protest songs spanning multiple decades. Using NLP and audio analysis, we identify the linguistic and musical features that differentiate protest songs. Instead of focusing on classification performance, we treat classification as a diagnostic tool to investigate these features and reveal broader patterns. Protest songs are not just politically charged they are acoustically and linguistically distinct, and we quantify how.

Abstract

Generation Alpha (born 2010-2024) is the first generation fully raised within the digital ecosystem. They exhibit unique linguistic behaviours influenced by rampant online communication and platform-specific cultures. This study examines the rapid evolution of Gen Alpha slang through a comparative analysis of Millennial and Gen Z vernacular. We identify three core linguistic patterns: extreme lexical compression, digital culture-driven semantic shifts and part-of-speech conversion. We construct a comprehensive slang corpus sourced from online platforms and evaluate the performance of four AI translation systems (viz. Google Translate, ChatGPT 4, Gemini 1.0, DeepSeek v3) on over 100 slang terms. Our results reveal significant translation challenges rooted in culturally-bound terms from gaming, meme culture, and mental health discourse. Most errors are the result of inadequate cultural contextualization, with literal translations dominating the error patterns. Our findings highlight the critical limitations in current language models and emphasize the need for adaptive, culturally sensitive and context-aware frameworks that can handle the dynamic lexicon of evolving youth vernacular.

Abstract

Emotion detection in text plays a very crucial role in NLP applications such as sentiment anal- ysis and feedback analysis. This study tackles two tasks: multi-label emotion detection, where the goal is to classify text based on six emotions (joy, sadness, fear, anger, surprise, and disgust) in a multilingual setting, and emotion intensity prediction, which assigns an ordinal intensity score to each of the above-given emotions. Using the BRIGHTER dataset, a multilingual corpus spanning 28 languages, the paper ad- dresses issues like class imbalances by treating each emotion as an independent binary classifi- cation problem. The paper first explores strate- gies such as static embeddings such as GloVe with logistic regression classifiers on top of it. To capture contextual nuances more effectively, we fine-tune transformer based models, such as BERT and RoBERTa. Our approach demon- strates the benefits of fine-tuning for improved emotion prediction, while also highlighting the challenges of multilingual and multi-label clas- sification.

Abstract

Machine Translation (MT) is an essential tool for communication among people across different cultures, yet Named Entity (NE) translation remains a major challenge due to its rarity in occurrence and ambiguity. Traditional approaches, like using lexicons or parallel corpora, often fail to generalize to unseen entities and, hence, do not perform well. To address this, we create a silver dataset using the Google Translate API and fine-tune the facebook/nllb200-distilled-600M model with LoRA (LowRank Adaptation) to enhance translation accuracy while also maintaining efficient memory use. Evaluated with metrics such as BLEU, COMET, and M-ETA, our results show that fine-tuning a specialized MT model improves NE translation without having to rely on largescale general-purpose models.

Abstract

Foundational Language Models perform significantly better on downstream tasks in specialised domains (such as law, computer science, and medical science) upon being further pre-trained on extensive domain-specific corpora, but this continual pre-training incurs heavy computational costs. Indeed, some of the most performant specialised language models such as BioBERT incur even higher computing costs during domain-specific training than the pre-training cost of the foundational models they are initialised from. In this paper, we argue that much of the extended pre-training is redundant, with models seemingly wasting valuable resources re-learning lexical and semantic patterns already well-represented in their foundational models such as BERT, T5 and GPT. Focusing on Masked Language Models, we introduce a novel domain-specific masking strategy that is designed to facilitate continual learning while minimizing the training cost. Using this approach, we train and present a BERT-based model trained on a biomedical corpus that matches or surpasses traditionally trained biomedical language models in performance across several downstream classification tasks while incurring up to 11 times lower training costs.

Abstract

Foundational Language Models perform significantly better on downstream tasks in the biomedical domain upon being further pre-trained on extensive biomedical corpora, but this continual pre-training incurs heavy computational costs. Indeed, some of the most performant biomedical language models incur even more computing costs during domain-specific training than the entire training cost of the foundational models they are warm-started from. In this paper, we argue that much of the extended pre-training is redundant, with models seemingly wasting valuable resources re-learning lexical and semantic patterns already well-represented in their foundational models such as BERT, T5 and GPT. Focusing on Masked Language Models, we introduce a novel domain-specific masking strategy that is designed to facilitate continual learning while minimizing the training cost. Using this approach, we train and present a BERT-based model that matches or surpasses traditionally trained biomedical language models in performance across several downstream classification tasks while incurring up to 11 times lower training costs.

Abstract

According to the cooperative principle (Grice 1975), utterances in a conversation adhere to the four maxims of conversational implicature – quantity, relation, quality and manner. An implicature arises when a maxim is flouted or contradicted, and the speaker is forced to infer a meaning. Consider the example: Speaker A: का िदलीप को परीकाएं आसान लग रही है? Kya Dilip ko parikshayein aasan lag rahi hain? (Does Dilip find the exam easy?) Speaker B: आज-कल वो सो नही पा रहा | [Aaj-kal wo so nahi pa raha.] (He hasn’t been able to sleep lately.) Speaker B flouts the maxim of relevance by not answering the question with a yes or no. Instead, his reply that Dilip has trouble sleeping implies that he doesn’t find the exams to be easy. While computational approaches have made some progress in English implicature benchmarks - GRICE (Zheng, 2021), IMPRESS (Jeretic 2020) and Conversational Implicature benchmark (CIB) (George and Mamidi, 2020), there are no benchmarks for Hindi. Hence, we propose a Hindi implicature benchmark- Indirect Questions Hi Implicature Benchmark - Toledo-Ronen (2020) found that translation- based methods lose nuances in argument evaluation. XCOPA benchmark (Ponti 2020) & Huang (2024) demonstrated that translated data hurts model performance for structurally divergent non- English languages. Hindi is a free word order, morphologically rich language. It is structurally different from English hence, translating English implicature benchmarks may not be unsuitable. Wang (2024) present indirect answers to yes-no questions in dialogues benchmark. Similar to their approach, we plan to collect Hindi interviews and extract indirect questions along with their answers involving implicature. We will annotate 5000 such questions. We will maintain a constraint that previous dialogues will be added to the question as a background to simplify the inference over a single dialogue turn. Evaluation will rely on ROUGE scores to measure correctness, as we leave exploration of suitable metrics for future work. For model evaluation, we plan to leverage a) English LLMs (meta-llama/Llama-3.3-70B (Grattafiori 2024) and the current state-of-the-art reported by Sravanthi (2024), FlanT5-XXL (Chung 2022) finetuned on Hindi translated GRICE (Zheng 2021), IMPRESS (Jeretic 2020) and CIB (George and Mamidi 2020) and b) Hindi LLMs (Llama-3-Nanda-10B-Chat (Choudhury 2024) and Airavata (Gala 2024)).

Abstract

Computational Linguistics is an interdisciplinary field of computer science and linguistics that focuses on designing computational models and algorithms for processing, analyzing, and generating human language. Over recent years, this field has made substantial progress. While its primary emphasis tends to center around widely spoken languages, there is equal importance in investigating languages that are not commonly spoken but have contributed immensely to the literature, culture, and philosophy of the society. Thus, this survey article comprehensively delves into the exploration of computational tasks undertaken for Sanskrit, an ancient language of the Indian sub-continent steeped in a wealth of literary heritage. The purpose of this study is to provide an overview of the progress made thus far in the computational analysis of Sanskrit, while also reviewing the current digital infrastructure that supports these efforts. Additionally, our study also identifies potential avenues for future research, serving as a reference for anyone interested in advancing their exploration in this field.

Abstract

There is a high prevalence of disinformation and manipulative narratives in online new sources today, and verification of its informative integrity is a vital need as online audience is highly susceptible to being affected by such propaganda or disinformation. The task of verifying any online information is, however, a significant challenge. The task Multilingual Characterization and Extraction of Narratives from Online News, therefore focuses on developing novel methods of analyzing news ecosystems and detecting manipulation attempts to address this challenge. As a part of this effort, we focus on the subtask of Entity Framing, which involves assigning named entities in news articles one of three main roles ( Protagonist, Antagonist, and Innocent) with a further fine-grained role distinction. We propose a pipeline that involves summarizing the article with the summary being centered around the entity. The entity and its entity-centric summary is then used as input for a BERT-based classifier to carry out the final role classification. Finally, we experiment with different approaches in the steps of the pipeline and compare the results obtained by them.

Abstract

This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models’ capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs’ understanding of implicature and facilitate better human-computer interaction.