30 research outputs found
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering
Visual Question Answering (VQA) is a task that requires computers to give
correct answers for the input questions based on the images. This task can be
solved by humans with ease but is a challenge for computers. The
VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the
multilingual domain on a newly released dataset: UIT-EVJVQA, in which the
questions and answers are written in three different languages: English,
Vietnamese and Japanese. We approached the challenge as a sequence-to-sequence
learning task, in which we integrated hints from pre-trained state-of-the-art
VQA models and image features with Convolutional Sequence-to-Sequence network
to generate the desired answers. Our results obtained up to 0.3442 by F1 score
on the public test set, 0.4210 on the private test set, and placed 3rd in the
competition.Comment: VLSP2022-EVJVQ
A Text-based Approach For Link Prediction on Wikipedia Articles
This paper present our work in the DSAA 2023 Challenge about Link Prediction
for Wikipedia Articles. We use traditional machine learning models with POS
tags (part-of-speech tags) features extracted from text to train the
classification model for predicting whether two nodes has the link. Then, we
use these tags to test on various machine learning models. We obtained the
results by F1 score at 0.99999 and got 7th place in the competition. Our source
code is publicly available at this link:
https://github.com/Tam1032/DSAA2023-Challenge-Link-prediction-DS-UIT_SATComment: Accepted by DSAA 2023 Conference in the DSAA Student Competition
Sectio
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education
Machine reading comprehension has been an interesting and challenging task in
recent years, with the purpose of extracting useful information from texts. To
attain the computer ability to understand the reading text and answer relevant
information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for
the task of multiple-choice reading comprehension in Vietnamese Textbooks which
contain the reading articles for students from Grade 1 to Grade 12. This
dataset has 699 reading passages which are prose and poems, and 5,273
questions. The questions in the new dataset are not fixed with four options as
in the previous version. Moreover, the difficulty of questions is increased,
which challenges the models to find the correct choice. The computer must
understand the whole context of the reading passage, the question, and the
content of each choice to extract the right answers. Hence, we propose the
multi-stage approach that combines the multi-step attention network (MAN) with
the natural language inference (NLI) task to enhance the performance of the
reading comprehension model. Then, we compare the proposed methodology with the
baseline BERTology models on the new dataset and the ViMMRC 1.0. Our
multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34%
better than the highest BERTology models. From the results of the error
analysis, we found the challenge of the reading comprehension models is
understanding the implicit context in texts and linking them together in order
to find the correct answers. Finally, we hope our new dataset will motivate
further research in enhancing the language understanding ability of computers
in the Vietnamese language
Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection
Hate-speech detection on social network language has become one of the main
researching fields recently due to the spreading of social networks like
Facebook and Twitter. In Vietnam, the threat of offensive and harassment cause
bad impacts for online user. The VLSP - Shared task about Hate Speech Detection
on social networks showed many proposed approaches for detecting whatever
comment is clean or not. However, this problem still needs further researching.
Consequently, we compare traditional machine learning and deep learning on a
large dataset about the user's comments on social network in Vietnamese and
find out what is the advantage and disadvantage of each model by comparing
their accuracy on F1-score, then we pick two models in which has highest
accuracy in traditional machine learning models and deep neural models
respectively. Next, we compare these two models capable of predicting the right
label by referencing their confusion matrices and considering the advantages
and disadvantages of each model. Finally, from the comparison result, we
propose our ensemble method that concentrates the abilities of traditional
methods and deep learning methods.Comment: Published in The 2020 RIVF International Conference on Computing and
Communication Technologies (RIVF