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Found 6 papers, 2 papers with code
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences.
Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
In this work, we propose a new pipeline using ChatGPT instead of human experts to generate high-quality feedback data for improving factual consistency in the clinical note summarization task.
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text
This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context.
STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning
STEPs-RL is trained in a supervised manner to predict the phonetic sequence of a target spoken-word using its contextual spoken word's speech and text, such that the model encodes its meaningful latent representations.
Contextualized Spoken Word Representations from Convolutional Autoencoders
A lot of work has been done to build text-based language models for performing different NLP tasks, but not much research has been done in the case of audio-based language models.
Correlated Feature Selection for Tweet Spam Classification
This step is necessary as we can reduce the training time if we combine the highly correlated features.
