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Found 16 papers, 4 papers with code
Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically
Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field.
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering
It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems.
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation.
A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future
Chain-of-thought reasoning, a cognitive process fundamental to human intelligence, has garnered significant attention in the realm of artificial intelligence and natural language processing.
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps.
Mixture of Experts for Biomedical Question Answering
In this paper, in order to alleviate the parameter competition problem, we propose a Mixture-of-Expert (MoE) based question answering method called MoEBQA that decouples the computation for different types of questions by sparse routing.
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning
Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks
Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences.
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training.
MedWriter: Knowledge-Aware Medical Text Generation
To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical.
Event Extraction as Multi-turn Question Answering
Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels.
Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks
In this paper, we propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN), which can utilize the topic information hidden in the articles to guide the generation of pertinent and diversified comments.
Neural Data-to-Text Generation with Dynamic Content Planning
We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text.
