MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures
- URL: http://arxiv.org/abs/2410.15463v1
- Date: Sun, 20 Oct 2024 18:29:38 GMT
- Title: MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures
- Authors: Aizan Zafar, Kshitij Mishra, Asif Ekbal,
- Abstract summary: We propose a novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers.
This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging.
- Score: 24.262037382512975
- License:
- Abstract: In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MedLogic-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MedLogic-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MedLogic-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness
Related papers
- Generating Explanations in Medical Question-Answering by Expectation
Maximization Inference over Evidence [33.018873142559286]
We propose a novel approach for generating natural language explanations for answers predicted by medical QA systems.
Our system extract knowledge from medical textbooks to enhance the quality of explanations during the explanation generation process.
arXiv Detail & Related papers (2023-10-02T16:00:37Z) - Modeling Hierarchical Reasoning Chains by Linking Discourse Units and
Key Phrases for Reading Comprehension [80.99865844249106]
We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning.
Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
arXiv Detail & Related papers (2023-06-21T07:34:27Z) - Complex Query Answering on Eventuality Knowledge Graph with Implicit
Logical Constraints [48.831178420807646]
We propose a new framework to leverage neural methods to answer complex logical queries based on an EVentuality-centric KG.
Complex Eventuality Query Answering (CEQA) considers the implicit logical constraints governing the temporal order and occurrence of eventualities.
We also propose a Memory-Enhanced Query (MEQE) to significantly improve the performance of state-of-the-art neural query encoders on the CEQA task.
arXiv Detail & Related papers (2023-05-30T14:29:24Z) - Discourse-Aware Graph Networks for Textual Logical Reasoning [142.0097357999134]
Passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence)
We propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs)
The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features.
arXiv Detail & Related papers (2022-07-04T14:38:49Z) - Question Answering over Knowledge Bases by Leveraging Semantic Parsing
and Neuro-Symbolic Reasoning [73.00049753292316]
We propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering(NSQA) system.
NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0.
arXiv Detail & Related papers (2020-12-03T05:17:55Z) - Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex
Healthcare Question Answering [89.76059961309453]
HeadQA dataset contains multiple-choice questions authorized for the public healthcare specialization exam.
These questions are the most challenging for current QA systems.
We present a Multi-step reasoning with Knowledge extraction framework (MurKe)
We are striving to make full use of off-the-shelf pre-trained models.
arXiv Detail & Related papers (2020-08-06T02:47:46Z) - VQA-LOL: Visual Question Answering under the Lens of Logic [58.30291671877342]
We investigate whether visual question answering systems trained to answer a question about an image, are able to answer the logical composition of multiple such questions.
We construct an augmentation of the VQA dataset as a benchmark, with questions containing logical compositions and linguistic transformations.
We propose our Lens of Logic (LOL) model which uses question-attention and logic-attention to understand logical connectives in the question, and a novel Fr'echet-Compatibility Loss.
arXiv Detail & Related papers (2020-02-19T17:57:46Z)
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.