6 research outputs found
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. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graphbased similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub
HunSum-1 : an abstractive summarization dataset for Hungarian
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 from 9 major Hungarian news sites through CommonCrawl. Using this dataset, we build abstractive summarizer models based on huBERT and mT5. We demonstrate the value of the created dataset by performing a quantitative and qualitative analysis on the models’ results. The HunSum-1 dataset, all models used in our experiments and our code1 are available open source
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. We extract corresponding
subtrees from the dependency parse trees of the source and target sentences and
swap these across bisentences to create augmented samples. We perform thorough
filtering based on graphbased similarities of the dependency trees and
additional heuristics to ensure that extracted subtrees correspond to the same
meaning. We conduct resource-constrained experiments on 4 language pairs in
both directions using the IWSLT text translation datasets and the Hunglish2
corpus. The results demonstrate consistent improvements in BLEU score over our
baseline models in 3 out of 4 language pairs. Our code is available on GitHub