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Found 7 papers, 3 papers with code
HunSum-1: an Abstractive Summarization Dataset for Hungarian
We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1. 14M news articles.
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.
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.
A Three Step Training Approach with Data Augmentation for Morphological Inflection
We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task.
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features.
BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection
We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task.
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.
