Codemixing, the linguistic phenomenon where a speaker alternates between two or more languages within a conversation or even a single utterance, presents a significant challenge for machine translation systems due to its syntactic complexity and contextual nuances. This paper introduces a set of advanced transformerbased models fine-tuned specifically for translating codemixed text to English, more specifically, Hindi-English (colloquially referred to as Hinglish) codemixed text into English. Unlike standard bilingual corpora, codemixed data requires an understanding of the intricacies of grammatical structures and cultural contexts embedded within the language blend. Existing machine translation efforts in codemixed languages have largely been constrained by the paucity of robust datasets and models that can capture the nuanced semantic and syntactic interplay characteristic of such languages. We present a novel dataset PACMAN trans for Hinglish to English machine translation, based on the PACMAN strategy, meticulously curated to represent natural codemixing patterns. Our generic fine-tuned translation models trained on the novel data outperforms current state-of-theart Large Language Models (LLMs) by 38% in terms of BLEU score. Further, when fine-tuned on custom benchmark datasets, our focused dual fine-tuned models surpass the PHINC dataset BLEU score benchmark by 22%. Our comparative analysis illustrates significant improvements in translation quality, showcasing the potential of fine-tuning transformer models in bridging the linguistic divide in codemixed language translation. The success of our models reflects a promising step forward in the quest to provide seamless translation services for the ever-growing multilingual population and the complex linguistic phenomena they generate.

Code-mixing or Code-switching is the mixing of languages in the same context, predominantly observed in multilingual societies. The existing code-mixed datasets are small and primarily contain social media text that does not adhere to standard spelling and grammar. Computational models built on such data fail to generalise on unseen code-mixed data. To address the unavailability of quality code-mixed annotated datasets, we explore the combined task of generating annotated code mixed data, and building computational models from this generated data, specifically for code-mixed Part-Of-Speech (POS) tagging. We introduce PACMAN(PArallel CodeMixed dAta generatioN) - a synthetically generated code-mixed POS tagged dataset, with above 50K samples, which is the largest annotated code-mixed dataset. We build POS taggers using classical machine learning and deep learning based techniques on the generated data to report an F1-score of 98% (8% above current State-of-the-art (SOTA)). To determine the efficacy of our data, we compare it against the existing benchmark in code-mixed POS tagging. PACMAN outperforms the benchmark, ratifying that our dataset and, subsequently, our POS tagging models are generalised and capable of handling even natural code-mixed and monolingual data.

Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes- with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes,overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.