Croatia

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.

EACL 2023

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

grac

word sense

semantic vector space model

ukrainian

language variation

corpus linguistics

### 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.

workshop paper

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.

A Low-Resource Approach to the Grammatical Error Correction of Ukrainian

This paper presents the results of the UNLP 2023 shared task, the first Shared Task on Grammatical Error Correction for the Ukrainian language. The task included two tracks: GEC-only and GEC+Fluency. The dataset and evaluation scripts were provided to the participants, and the final results were evaluated on a hidden test set. Six teams submitted their solutions before the deadline, and four teams submitted papers that were accepted to appear in the UNLP workshop proceedings and are referred to in this report. The CodaLab leaderboard is left open for further submissions.

The UNLP 2023 Shared Task on Grammatical Error Correction for Ukrainian

For monitoring crises, political events are extracted from the news. The large amount of unstructured full-text event descriptions makes a case-by-case analysis unmanageable, particularly for low-resource humanitarian aid organizations. This creates a demand to classify events into event types, a task referred to as event coding. Typically, domain experts craft an event type ontology, annotators label a large dataset and technical experts develop a supervised coding system. In this work, we propose PR-ENT, a new event coding approach that is more flexible and resource-efficient, while maintaining competitive accuracy.

Rethinking the Event Coding Pipeline with Prompt Entailment

An approach to improve question-answering performance is to retrieve accompanying information that contains factual evidence matching the question. These retrieved documents are then fed into a reader that generates an answer. A commonly applied retriever is dense passage retrieval. In this retriever, the output of a transformer neural network is used to query a knowledge database for matching documents. Inspired by the observation that different layers of a transformer network provide rich representations with different levels of abstraction, we hypothesize that useful queries can be generated not only at the output layer, but at every layer of a transformer network, and that the hidden representations of different layers may combine to improve the fetched documents for reader performance. Our novel approach integrates retrieval into each layer of a transformer network, exploiting the hierarchical representations of the input question. We show that our technique outperforms prior work on downstream tasks such as question answering, demonstrating the effectiveness of our approach.

Hierarchical Representations in Dense Passage Retrieval for Question-Answering

Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to extract concise claims based on medical entities in the text. However, their study has two limitations: First, it relies on gold-annotated entities. Therefore, its feasibility for a real-world application cannot be assessed since this requires detecting relevant entities automatically. Second, they represent claim entities with the original tokens. This constitutes a terminology mismatch which potentially limits the fact-checking performance. To understand both challenges, we propose a claim extraction pipeline for medical tweets that incorporates named entity recognition and terminology normalization via entity linking. We show that automatic NER does lead to a performance drop in comparison to using gold annotations but the fact-checking performance still improves considerably over inputting the unchanged tweets. Normalizing entities to their canonical forms does, however, not improve the performance.

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