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Found 12 papers, 9 papers with code
BME-TUW at SR’20: Lexical grammar induction for surface realization
We present a system for mapping Universal Dependency structures to raw text which learns to restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes a mapping between string and graph operations.
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.
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.
Evaluating Transferability of BERT Models on Uralic Languages
Transformer-based language models such as BERT have outperformed previous models on a large number of English benchmarks, but their evaluation is often limited to English or a small number of well-resourced languages.
Automatic punctuation restoration with BERT models
We present an approach for automatic punctuation restoration with BERT models for English and Hungarian.
