3,526 research outputs found
An Effective Method using Phrase Mechanism in Neural Machine Translation
Machine Translation is one of the essential tasks in Natural Language
Processing (NLP), which has massive applications in real life as well as
contributing to other tasks in the NLP research community. Recently,
Transformer -based methods have attracted numerous researchers in this domain
and achieved state-of-the-art results in most of the pair languages. In this
paper, we report an effective method using a phrase mechanism,
PhraseTransformer, to improve the strong baseline model Transformer in
constructing a Neural Machine Translation (NMT) system for parallel corpora
Vietnamese-Chinese. Our experiments on the MT dataset of the VLSP 2022
competition achieved the BLEU score of 35.3 on Vietnamese to Chinese and 33.2
BLEU scores on Chinese to Vietnamese data. Our code is available at
https://github.com/phuongnm94/PhraseTransformer
No-arbitrage condition and existence of equilibrium with dividends
In this paper we first give an elementary proof of existence of equilibrium with dividends in an economy with possibly satiated consumers.We then introduce a no-arbitrage condition and show that it is equivalent to the existence of equilibrium with dividends.equilibrium with dividends, economy with possibly satiated consumers, no-arbitrage condition
Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022
We introduce efficient deep learning-based methods for legal document
processing including Legal Document Retrieval and Legal Question Answering
tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In
this competition, we achieve 1\textsuperscript{st} place in the first task and
3\textsuperscript{rd} place in the second task. Our method is based on the
XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus
before fine-tuning to the specific tasks. The experimental results showed that
our method works well in legal retrieval information tasks with limited labeled
data. Besides, this method can be applied to other information retrieval tasks
in low-resource languages
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