Croatia

In order for NLP technology to be widely applicable and useful, it needs to be inclusive of users across the world&#39;s languages, equitable, i.e., not unduly biased towards any particular language, and accessible to users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluation paradigm that assesses NLP technologies across all three dimensions, hence quantifying the diversity of users they can serve. While inclusion and accessibility have received attention in recent literature, equity is currently unexplored. We propose to address this gap using the Gini coefficient, a well-established metric used for estimating societal wealth inequality. Using our paradigm, we highlight the distressed state of diversity of current technologies for Indian (IN) languages, motivated by their linguistic diversity and large, varied speaker population. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation and also propose a novel approach to optimal resource allocation during fine-tuning. Finally, we discuss steps that must be taken to mitigate these biases and call upon the community to incorporate our evaluation paradigm when building linguistically diverse technologies.

EACL 2023

Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages

evaluation

multilingual

low-resource

In order for NLP technology to be widely applicable and useful, it needs to be inclusive of users across the world's languages, equitable, i.e., not unduly biased towards any particular language, and accessible to users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluation paradigm that assesses NLP technologies across all three dimensions, hence quantifying the diversity of users they can serve. While inclusion and accessibility have received attention in recent literature, equity is currently unexplored. We propose to address this gap using the Gini coefficient, a well-established metric used for estimating societal wealth inequality. Using our paradigm, we highlight the distressed state of diversity of current technologies for Indian (IN) languages, motivated by their linguistic diversity and large, varied speaker population. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation and also propose a novel approach to optimal resource allocation during fine-tuning. Finally, we discuss steps that must be taken to mitigate these biases and call upon the community to incorporate our evaluation paradigm when building linguistically diverse technologies.

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.

This study addresses the challenges of learning unsupervised word representations for the morphologically rich and low-resource Ukrainian language. Traditional models that perform decently on English do not generalize well for such languages due to a lack of sufficient data and the complexity of their grammatical structures. To overcome these challenges, we utilized a high-quality, large dataset of different genres for learning Ukrainian word vector representations. We found the best hyperparameters to train fastText language models on this dataset and performed intrinsic and extrinsic evaluations of the generated word embeddings using the established methods and metrics. The results of this study indicate that the trained vectors exhibit superior performance on intrinsic tests in comparison to existing embeddings for Ukrainian. Our best model gives 62% Accuracy on the word analogy task. Extrinsic evaluations were performed on two sequence labeling tasks: NER and POS tagging (83% spaCy NER F-score, 83% spaCy POS Accuracy, 92% Flair POS Accuracy).

Learning Word Embeddings for Ukrainian: A Comparative Study of FastText Hyperparameters

In the Russo-Ukrainian war, propaganda is produced by Russian state-run news outlets for both international and domestic audiences. Its content and form evolve and change with time as the war continues. This constitutes a challenge to content moderation tools based on machine learning when the data used for training and the current news start to differ significantly. In this follow-up study, we evaluate our previous BERT and SVM models that classify Pro-Kremlin propaganda from a Pro-Western stance, trained on the data from news articles and telegram posts at the start of 2022, on the new 2023 subset. We examine both classifiers' errors and perform a comparative analysis of these subsets to investigate which changes in narratives provoke drops in performance.

The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective

This paper addresses the need for massive corpora for a low-resource language and presents the publicly available UberText 2.0 corpus for the Ukrainian language and discusses the methodology of its construction. While the collection and maintenance of such a corpus is more of a data extraction and data engineering task, the corpus itself provides a solid foundation for natural language processing tasks. It can enable the creation of contemporary language models and word embeddings, resulting in a better performance of numerous downstream tasks for the Ukrainian language. In addition, the paper and software developed can be used as a guidance and model solution for other low-resource languages. The resulting corpus is available for download on the project page. It has 3.274 billion tokens, consists of 8.59 million texts and takes up 32 gigabytes of space.

Introducing UberText 2.0: A Corpus of Modern Ukrainian at Scale

This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9% accuracy for lexical meaning prediction for homonyms.

Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation

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.

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

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.

Downloads

Next from EACL 2023

Learning Word Embeddings for Ukrainian: A Comparative Study of FastText Hyperparameters

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from EACL 2023

Learning Word Embeddings for Ukrainian: A Comparative Study of FastText Hyperparameters

Downloads