Croatia

Despite recent NLP developments, abstractive summarization remains a challenging task, especially in the case of low-resource languages like Ukrainian. The paper aims at improving the quality of summaries produced by mT5 for news in Ukrainian by fine-tuning the model with a mixture of summarization and text similarity tasks using summary-article and titlearticle training pairs, respectively. The proposed training set-up with small, base, and large mT5 models produce higher quality résumé. Besides, we present a new Ukrainian dataset for the abstractive summarization task that consists of circa 36.5K articles collected from Hromadske.ua until June 2021.

EACL 2023

Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset

multilingual t5 model

ukrainian language

multi-task learning

abstractive summarization

workshop paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

We share the results of the project within the well-known Multi30k dataset edicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings.

Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian

Low-resource languages continue to present challenges for current NLP methods, and multilingual NLP is gaining attention in the research community. One of the main issues is the lack of sufficient high-quality annotated data for low-resource languages. In this work, we show how labeled data for high-resource languages such as English can be used in low-resource NLP. We present two silver datasets for coreference resolution in Ukrainian, adapted from existing English data by manual translation and machine translation in combination with automatic alignment and annotation projection. The code is made publicly available.

Silver Data for Coreference Resolution in Ukrainian: Translation, Alignment, and Projection

We present a corpus professionally annotated for grammatical error correction (GEC) and fluency edits in the Ukrainian language. We have built two versions of the corpus -- GEC+Fluency and GEC-only -- to differentiate the corpus application. To the best of our knowledge, this is the first GEC corpus for the Ukrainian language. We collected texts with errors (33,735 sentences) from a diverse pool of contributors, including both native and non-native speakers. The data cover a wide variety of writing domains, from text chats and essays to formal writing. Professional proofreaders corrected and annotated the corpus for errors relating to fluency, grammar, punctuation, and spelling. This corpus can be used for developing and evaluating GEC systems in Ukrainian. More generally, it can be used for researching multilingual and low-resource NLP, morphologically rich languages, document-level GEC, and fluency correction. The corpus is publicly available at \url{https://github.com/grammarly/ua-gec}.

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

The paper discusses a Semantic Vector Space Model targeted at revealing how Ukrainian word senses vary and relate to each other. One of the benefits of the proposed semantic model is that it considers second-order collocates of the words and, thus, has more potential to distinguish word senses observed in a unique concordance line, compared to the models that take into account only immediate collocates. Combined with the Multidimensional Scaling technique, this model allows for a lexicographer to explore the Ukrainian word senses distribution
on a large scale. The paper describes the first research results and the following steps of the initiative.

Exploring Word Sense Distribution in Ukrainian with a Semantic Vector Space Model

We describe a Ukrainian-Russian code-switching corpus of Ukrainian Parliamentary
Session Transcripts. The corpus includes speeches entirely in Ukrainian, Russian, or various types of mixed speech and allows us to see how speakers switch between these languages depending on the communicative situation. The paper describes the process of creating this corpus from the official multilingual transcripts using automatic language detecting and publicly available metadata on the speakers. On
this basis, we consider possible reasons for the change in the number of Ukrainian speakers in the parliament and present the most common patterns of bilingual Ukrainian and Russian code-switching in parliamentarians’ speeches.

The Parliamentary Code-Switching Corpus: Bilingualism in the Ukrainian Parliament in the 1990s-2020s

This paper presents an ongoing project to create the Ukrainian Brown Corpus (BRUK), a disambiguated corpus of Modern Ukrainian. Inspired by and loosely based on the original Brown University corpus, BRUK contains one million words, spans 11 years (2010–2020), and represents edited written Ukrainian. Using stratified random sampling, we have selected fragments of texts from multiple sources to ensure maximum variety, fill nine predefined categories, and produce a balanced corpus. BRUK has been automatically POS-tagged with the help of our tools (a large morphological dictionary of Ukrainian and a tagger). A manually disambiguated and validated subset of BRUK (450,000 words) has been made available online. This gold standard, the biggest of its kind for Ukrainian, fills a critical need in the NLP ecosystem for this language. The ultimate goal is to produce a fully disambiguated one-million corpus of Modern Ukrainian.

Creating a POS Gold Standard Corpus of Modern Ukrainian

The development of the RedPenNet architecture was an iterative process, involving numerous attempts and experiments to refine our approach. 
Let me share with you our journey through the dawn of the fine-tuning era, the architecture we employed for BEA-2019 Shared Task, the insights we gained from the outcomes and how it all led us to develop RedPenNet, which we successfully utilized in UNLP-2023 Shared Task.


RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans

We explore pretraining unidirectional language models on 4B tokens from the largest curated corpus of Ukrainian, UberText 2.0. We enrich document text by surrounding it with weakly structured metadata, such as title, tags, and publication year, enabling metadata-conditioned text generation and text-conditioned metadata prediction at the same time. We pretrain GPT-2 Small, Medium, and Large models on a single GPU, reporting training times, BPC on BrUK, BERTScore, and BLEURT on titles for 1000 News from the Future. Next, we venture to formatting POS and NER datasets as instructions, and train low-rank attention adapters, performing these tasks as constrained text generation. We release our models for the community at https://github.com/proger/uk4b.

GPT-2 Metadata Pretraining Towards Instruction Finetuning for Ukrainian

The task of Grammatical Error Correction (GEC) has been extensively studied in the English language. However, its application to low-resource languages, such as Ukrainian, remains an open challenge. In this paper, we develop sequence tagging and neural machine translation models for the Ukrainian language as well as a
set of algorithmic correction rules to augment those systems. We also develop synthetic data generation techniques for the Ukrainian language to create high-quality human-like errors. Finally, we determine the best combination of synthetically generated data to augment the existing UA-GEC corpus and achieve the state-of-the-art results of 0.663 F0.5 score on the newly established UA-GEC benchmark. The code and trained models are publicly available on GitHub and HuggingFace.

Comparative Study of Models Trained on Synthetic Data for Ukrainian Grammatical Error Correction

We present our system that participated in the
shared task on the grammatical error correction
of Ukrainian. We have implemented two ap-
proaches that make use of large pre-trained lan-
guage models and synthetic data, that have been
used for error correction of English as well as
low-resource languages. The first approach is
based on finetuning a large multilingual lan-
guage model (mT5) in two stages: first, on
synthetic data, and then on gold data. The sec-
ond approach trains a (smaller) seq2seq Trans-
former model pre-trained on synthetic data and
finetuned on gold data. Our mT5-based model
scored first in “GEC only” track, and a very
close second in the “GEC+Fluency” track. Our
two key innovations are (1) finetuning in stages,
first on synthetic, and then on gold data; and
(2) a high-quality corruption method based on
round-trip machine translation to complement
existing noisification approaches.

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Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset

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Extension Multi30K: Multimodal Dataset for Integrated Vision and Language Research in Ukrainian

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