2 citations · 3 across the 5 of their papers we have counts for
5 papers
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
Botond Barta, Dorina Lakatos, Attila Nagy +2
Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably…
TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping
Attila Nagy, Dorina Lakatos, Botond Barta +1
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-re…
Data Augmentation for Machine Translation via Dependency Subtree Swapping
Attila Nagy, Dorina Petra Lakatos, Botond Barta +2
We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependenc…
HunSum-1: an Abstractive Summarization Dataset for Hungarian
Botond Barta, Dorina Lakatos, Attila Nagy +2
We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1.14M news articles. The dataset is built by collecting, cleaning and deduplicating data fro…
A Three Step Training Approach with Data Augmentation for Morphological Inflection
Gabor Szolnok, Botond Barta, Dorina Lakatos +1
We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with th…