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Found 12 papers, 5 papers with code
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A~Case~Study~at~HCMUT
In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic.
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models
Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence.
LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training
Paraphrases are texts that convey the same meaning while using different words or sentence structures.
xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks
Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science.
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment
Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning.
Grounding Language Representation with Visual Object Information via Cross Modal Pretraining
However, this approach has two main drawbacks: (i) the whole image usually contains more objects and backgrounds than the sentence itself; thus, matching them together will confuse the grounded model; (ii) CNN only extracts the features of the image but not the relationship between objects inside that, limiting the grounded model to learn complicated contexts.
Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.
PageRank algorithm for Directed Hypergraph
In this paper, we will model the World Wide Web's link structure as the directed hypergraph.
Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors
The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets.
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media
However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels.
Towards Autoencoding Variational Inference for Aspect-based Opinion Summary
Firstly, we introduce the Autoencoding Variational Inference for Aspect Discovery (AVIAD) model, which extends the previous work of Autoencoding Variational Inference for Topic Models (AVITM) to embed prior knowledge of seed words.
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis
In particular, when analyzing the applications of deep learning in sentiment analysis, we found that the current approaches are suffering from the following drawbacks: (i) the existing works have not paid much attention to the importance of different types of sentiment terms, which is an important concept in this area; and (ii) the loss function currently employed does not well reflect the degree of error of sentiment misclassification.
