11 citations · 25 across the 12 of their papers we have counts for
13 papers
HuAMR: A Hungarian AMR Parser and Dataset
Botond Barta, Endre Hamerlik, Milán Konor Nyist +1
We present HuAMR, the first Abstract Meaning Representation (AMR) dataset and a suite of large language model-based AMR parsers for Hungarian, targeting the scarcity of semantic re…
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…
Morphosyntactic probing of multilingual BERT models
Judit Acs, Endre Hamerlik, Roy Schwartz +2
We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a se…
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…
Syntax-based data augmentation for Hungarian-English machine translation
Attila Nagy, Patrick Nanys, Balázs Frey Konrád +2
We train Transformer-based neural machine translation models for Hungarian-English and English-Hungarian using the Hunglish2 corpus. Our best models achieve a BLEU score of 40.0 on…
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…
Evaluating Transferability of BERT Models on Uralic Languages
Judit Ács, Dániel Lévai, András Kornai
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 smal…
Subword Pooling Makes a Difference
Judit Ács, Ákos Kádár, András Kornai
Contextual word-representations became a standard in modern natural language processing systems. These models use subword tokenization to handle large vocabularies and unknown word…
Evaluating Contextualized Language Models for Hungarian
Judit Ács, Dániel Lévai, Dávid Márk Nemeskey +1
We present an extended comparison of contextualized language models for Hungarian. We compare huBERT, a Hungarian model against 4 multilingual models including the multilingual BER…
Automatic punctuation restoration with BERT models
Attila Nagy, Bence Bial, Judit Ács
We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used bench…
The Role of Interpretable Patterns in Deep Learning for Morphology
Judit Acs, Andras Kornai
We examine the role of character patterns in three tasks: morphological analysis, lemmatization and copy. We use a modified version of the standard sequence-to-sequence model, wher…