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.

Chest X-ray radiology majorly focuses on diseases like consolidation, pneumothorax, pleural effusion, lung collapse, etc., causing breathing and circulation problems. A tendency to share such problems in the forums for an answer without revealing personal demographics is also very common. However, we have observed more visitors than authors, which leads to a very poor average reply per discussion (3 to 12 only), and also many left with no or late replies in the forums. To alleviate the process, and ease of acquiring the best replies from multiple discussions, we propose a supervised learning framework by automatic scrapping and annotation of breathing problem-related group discussions from the patient.info 1 forum and determine the associated sentiment of the most voted respondent post using Bi-LSTM. We assume the most voted reply is the most factual and experienced. We mainly scrapped and determined the sentiment of bronchiectasis, asthma, pneumonia, and respiratory diseaserelated posts. After filtering and augmentation, a total of 1,748 posts were used for training our Stacked Bi-LSTM model and achieved an overall accuracy of 90%.

Hate speech, offensive language, aggression, racism, sexism, and other abusive language is a common phenomenon in social media. There is a need for Artificial Intelligence (AI) based intervention which can filter hate content at scale. Most existing hate speech detection solutions have utilized the features by treating each post as an isolated input instance for the classification. This paper addresses this issue by introducing a unique model that improves hate speech identification for the English language by utilising intra-user and inter-user-based information. The experiment is conducted over single-task learning (STL) and multi-task learning (MTL) paradigms that use deep neural networks, such as convolution neural network (CNN), gated recurrent unit (GRU), bidirectional encoder representations from the transformer (BERT), and A Lite BERT (ALBERT). We use three benchmark datasets and conclude that combining certain user features with textual features gives significant improvements in macro-F1 and weightedF1.

Automated personality subtyping is a crucial area of research with diverse applications in psychology, healthcare, and marketing. However, current studies face challenges such as insufficient data, noisy text data, and difficulty in capturing complex personality traits. To address these issues, including empathy, distress, and emotion as auxiliary tasks in automated personality subtyping may enhance accuracy and robustness. This study introduces a Multi-input Multi-task Framework for Personality, Empathy, Distress, and Emotion Detection (MultiPEDE). This framework harnesses the complementary information from empathy, distress, and emotion tasks (auxiliary tasks) to enhance the accuracy and generalizability of automated personality subtyping (the primary task). The model uses a novel deep-learning architecture that captures the interdependencies between these constructs, is end-to-end trainable, and does not rely on ensemble strategies, making it practical for real-world applications. Performance evaluation involves labeled examples of five personality traits, two classes each for personality, empathy, and distress detection, and seven classes for emotion detection. This approach has diverse applications, including mental health diagnosis, improving online services, and aiding job candidate selection.

Multi-modal data analysis presents formidable challenges, as developing effective methods to capture correlations among different modalities remains an ongoing pursuit. In this study, we address multi-modal sentiment analysis through a novel quantum perspective. We propose that quantum principles, such as superposition, entanglement, and interference, offer a more comprehensive framework for capturing not only the cross-modal interactions between text, acoustics, and visuals but also the intricate relations within each modality. To empirically evaluate our approach, we employ the CMUMOSEI dataset as our testbed and utilize Qiskit by IBM to run our experiments on a quantum computer. Our proposed Quantum-Enhanced Multi-Modal Analysis Framework (QeMMA) showcases its significant potential by surpassing the baseline by 3.52% and 10.14% in terms of accuracy and F1 score, respectively, highlighting the promise of quantum-inspired methodologies.