Addressing the critical challenge of identifying and rectifying medical errors in clinical notes, we present a novel approach tailored for the MEDIQA-CORR task @ NAACL-ClinicalNLP 2024, which comprises three subtasks: binary classification, span identification, and natural language generation for error detection and correction. Binary classification involves detecting whether the text contains a medical error; span identification entails identifying the text span associated with any detected error; and natural language generation focuses on providing a free text correction if a medical error exists. Our proposed architecture leverages Named Entity Recognition (NER) for identifying disease-related terms, Retrieval-Augmented Generation (RAG) for contextual understanding from external datasets, and a quantized and fine-tuned Palmyra model for error correction. Our model achieved a global rank of 5 with an aggregate score of 0.73298, calculated as the mean of ROUGE-1-F, BERTScore, and BLEURT scores.

With so much information available online, it’s more important than ever to have reliable tools for summarizing text quickly and accurately. In this paper, we introduce a new way to improve the popular TextRank algorithm for extractive summarization. By adding a dynamic damping factor and using Latent Dirichlet Allocation (LDA) to enhance how text is represented, our method creates more meaningful summaries. We tested it with metrics like Pyramid, METEOR, and ROUGE, and compared it to the original TextRank. The results were promising, showing that our approach produces better summaries and could be useful for real-world applications like text mining and information retrieval.

This paper outlines our approach for the ERC subtask of the SemEval 2024 EdiREF Shared Task. In this sub-task, an emotion had to be assigned to an utterance which was the part of a dialogue. The utterance had to be classified into one of the following classes- disgust, contempt, anger, neutral, joy, sadness, fear, surprise. Our proposed system makes use of an ensemble of language specific RoBERTA and BERT models to tackle the problem. A weighted F1-score of 44% was achieved by our system in this task. We conducted comprehensive ablations and suggested directions of future work. Our codebase is available publicly.

This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing. Social media has accelerated the propagation of hate and violence-inciting speech in society. It is essential to develop efficient mechanisms to detect and curb the propagation of such texts. The problem of detecting violence-inciting texts is further exacerbated in low-resource settings due to sparse research and less data. The data provided in the shared task consists of texts in the Bangla language, where each example is classified into one of the three categories defined based on the types of violence-inciting texts. We try and evaluate several BERT-based models, and then use an ensemble of the models as our final submission. Our submission is ranked 10th in the final leaderboard of the shared task with a macro F1 score of 0.737.

With the ever-expanding pool of information accessible on the Internet, it has become increasingly challenging for readers to sift through voluminous data and derive meaningful insights. This is particularly noteworthy and critical in the context of documents such as financial reports and large-scale media reports. In the realm of finance, documents are typically lengthy and comprise numerical values. This research delves into the extraction of insights through text summaries from financial data, based on the user’s interests, and the identification of clues from these insights. This research presents a straightforward, allencompassing framework for conducting querybased summarization of financial documents, as well as analyzing the sentiment of the summary. The system’s performance is evaluated using benchmarked metrics, and it is compared to State-of-The-Art (SoTA) algorithms. Extensive experimentation indicates that the proposed system surpasses existing pre-trained language models.

Lexical Simplification is a challenging task that aims to improve the readability of text for nonnative people, people with dyslexia, and any linguistic impairments. It consists of 3 components: 1) Complex Word Identification 2) Substitute Generation 3) Substitute Ranking. Current methods use contextual information as a primary source in all three stages of the simplification pipeline. We argue that while context is an important measure, it alone is not sufficient in the process. In the complex word identification step, contextual information is inadequate, moreover, heavy feature engineering is required to use additional linguistic features. This paper presents a novel architecture for complex word identification that uses a pre-trained transformer model’s information flow through its hidden layers as a feature representation that implicitly encodes all the features required for identification. We portray how database methods and masked language modeling can be complementary to one another in substitute generation and ranking process that is built on the foundational pillars of Simplicity, Grammatical and Semantic correctness, and context preservation. We show that our proposed model generalizes well and outperforms the current state-of-the-art on wellknown datasets.

In recent years, the rise of multimedia content on the internet has inundated users with a vast and diverse array of information, including images, videos, and textual data. Handling this flood of multimedia data necessitates advanced techniques capable of distilling this wealth of information into concise, meaningful summaries. Multimodal summarization, which involves generating summaries from multiple modalities such as text, images, and videos, has become a pivotal area of research in natural language processing, computer vision, and multimedia analysis. This survey paper offers an overview of the state-of-the-art techniques, methodologies, and challenges in the domain of multimodal summarization. We highlight the interdisciplinary advancements made in this field specifically on the lines of two main frontiers:1) Multimodal Abstractive Summarization, and 2) Pre-training Language Models in Multimodal Summarization. By synthesizing insights from existing research, we aim to provide a holistic understanding of multimodal summarization techniques.

This paper presents our approach for the WASSA 2023 Empathy, Emotion and Personality Shared Task. Empathy and distress are human feelings that are implicitly expressed in natural discourses. Empathy and distress detection are crucial challenges in Natural Language Processing that can aid our understanding of conversations. The provided dataset consists of several long-text examples in the English language, with each example associated with a numeric score for empathy and distress. We experiment with several BERT-based models as a part of our approach. We also try various ensemble methods. Our final submission has a Pearson’s r score of 0.346, placing us third in the empathy and distress detection subtask.