Sentiment Reasoning for Healthcare

• URL: http://arxiv.org/abs/2407.21054v3
• Date: Fri, 11 Oct 2024 05:43:19 GMT
• Title: Sentiment Reasoning for Healthcare
• Authors: Khai-Nguyen Nguyen, Khai Le-Duc, Bach Phan Tat, Duy Le, Long Vo-Dang, Truong-Son Hy,
• Abstract summary: Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. Our study conducted on both human transcripts and Automatic Speech Recognition (ASR) transcripts shows that Sentiment Reasoning helps improve model transparency by providing rationale for model prediction with quality semantically comparable to humans.
• Score: 2.0451307225357427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transparency in AI healthcare decision-making is crucial for building trust among AI and users. Incorporating reasoning capabilities enables Large Language Models (LLMs) to understand emotions in context, handle nuanced language, and infer unstated sentiments. In this work, we introduce a new task -- Sentiment Reasoning -- for both speech and text modalities, along with our proposed multimodal multitask framework and dataset. Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. Our study conducted on both human transcripts and Automatic Speech Recognition (ASR) transcripts shows that Sentiment Reasoning helps improve model transparency by providing rationale for model prediction with quality semantically comparable to humans while also improving model performance (1% increase in both accuracy and macro-F1) via rationale-augmented fine-tuning. Also, no significant difference in the semantic quality of generated rationales between human and ASR transcripts. All code, data (English-translated and Vietnamese) and models are published online: https://github.com/leduckhai/MultiMed.

Related papers

• Bias-Free Sentiment Analysis through Semantic Blinding and Graph Neural Networks [0.0]
  The SProp GNN relies exclusively on syntactic structures and word-level emotional cues to predict emotions in text. By semantically blinding the model to information about specific words, it is robust to biases such as political or gender bias. The SProp GNN shows performance superior to lexicon-based alternatives on two different prediction tasks, and across two languages.
  arXiv  Detail & Related papers  (2024-11-19T13:23:53Z)
• MindSpeech: Continuous Imagined Speech Decoding using High-Density fNIRS and Prompt Tuning for Advanced Human-AI Interaction [0.0]
  This paper reports a novel method for human-AI interaction by developing a direct brain-AI interface. We discuss a novel AI model, called MindSpeech, which enables open-vocabulary, continuous decoding for imagined speech. We demonstrate significant improvements in key metrics, such as BLEU-1 and BERT P scores, for three out of four participants.
  arXiv  Detail & Related papers  (2024-07-25T16:39:21Z)
• Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion Recognition [17.568724398229232]
  Speech emotion recognition (SER) has gained significant attention due to its several application fields, such as mental health, education, and human-computer interaction. This study proposes an iterative feature boosting approach for SER that emphasizes feature relevance and explainability to enhance machine learning model performance. The effectiveness of the proposed method is validated on the SER benchmarks of the Toronto emotional speech set (TESS), Berlin Database of Emotional Speech (EMO-DB), Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and Surrey Audio-Visual Expressed Emotion (SAVEE) datasets.
  arXiv  Detail & Related papers  (2024-06-01T00:39:55Z)
• Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue [71.15186328127409]
  Paralinguistics-enhanced Generative Pretrained Transformer (ParalinGPT) Model takes the conversational context of text, speech embeddings, and paralinguistic attributes as input prompts within a serialized multitasking framework. We utilize the Switchboard-1 corpus, including its sentiment labels as the paralinguistic attribute, as our spoken dialogue dataset.
  arXiv  Detail & Related papers  (2023-12-23T18:14:56Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Disentangled Variational Autoencoder for Emotion Recognition in Conversations [14.92924920489251]
  We propose a VAD-disentangled Variational AutoEncoder (VAD-VAE) for Emotion Recognition in Conversations (ERC) VAD-VAE disentangles three affect representations Valence-Arousal-Dominance (VAD) from the latent space. Experiments show that VAD-VAE outperforms the state-of-the-art model on two datasets.
  arXiv  Detail & Related papers  (2023-05-23T13:50:06Z)
• Bridging Speech and Textual Pre-trained Models with Unsupervised ASR [70.61449720963235]
  This work proposes a simple yet efficient unsupervised paradigm that connects speech and textual pre-trained models. We show that unsupervised automatic speech recognition (ASR) can improve the representations from speech self-supervised models. Notably, on spoken question answering, we reach the state-of-the-art result over the challenging NMSQA benchmark.
  arXiv  Detail & Related papers  (2022-11-06T04:50:37Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses [66.49753193098356]
  We investigate the reason behind the surprising adversarial brittleness of scoring models. Our results indicate that autoscoring models, despite getting trained as "end-to-end" models, behave like bag-of-words models. We propose detection-based protection models that can detect oversensitivity and overstability causing samples with high accuracies.
  arXiv  Detail & Related papers  (2021-09-24T03:49:38Z)
• Leveraging Pre-trained Language Model for Speech Sentiment Analysis [58.78839114092951]
  We explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis. We propose a pseudo label-based semi-supervised training strategy using a language model on an end-to-end speech sentiment approach.
  arXiv  Detail & Related papers  (2021-06-11T20:15:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.