Search Results for author:
Found 8 papers, 6 papers with code
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models.
TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping
Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages.
Data Augmentation for Machine Translation via Dependency Subtree Swapping
We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation.
HunSum-1: an Abstractive Summarization Dataset for Hungarian
We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1. 14M news articles.
Syntax-based data augmentation for Hungarian-English machine translation
We train Transformer-based neural machine translation models for Hungarian-English and English-Hungarian using the Hunglish2 corpus.
Developing neural machine translation models for Hungarian-English
I start my thesis with a detailed literature review on neural networks, sequential modeling, neural machine translation, dependency parsing and data augmentation.
Improving the sample-efficiency of neural architecture search with reinforcement learning
In our research, we propose to modify this to a more modern and complex algorithm, PPO, which has demonstrated to be faster and more stable in other environments.
Automatic punctuation restoration with BERT models
We present an approach for automatic punctuation restoration with BERT models for English and Hungarian.
