Croatia

We present lemmatization experiments on the unstandardized low-resourced languages Low Saxon and Occitan using two machine-learning-based approaches represented by MaChAmp and Stanza. We show different ways to increase training data by leveraging historical corpora, small amounts of gold data and dictionary information, and discuss the usefulness of this additional data. In the results, we find some differences in the performance of the models depending on the language. This variation is likely to be partly due to differences in the corpora we used, such as the amount of internal variation. However, we also observe common tendencies, for instance that sequential models trained only on gold-annotated data often yield the best overall performance and generalize better to unknown tokens.

EACL 2023

Lemmatization Experiments on Two Low-Resourced Languages: Low Saxon and Occitan

low saxon

low-resourced languages

occitan

lemmatization

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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.

The study of low-resourced East Slavic lects is becoming increasingly relevant as they face the prospect of extinction under the pressure of standard Russian while being treated by academia as an inferior part of this lect. The Khislavichi lect, spoken in a settlement on the border of Russia and Belarus, is a perfect example of such an attitude.

We take an alternative approach and study East Slavic lects (such as Khislavichi) as separate systems. The proposed method includes the development of a tagged corpus through morphological tagging with the models trained on the bigger lects. Morphological tagging results may be used to place these lects among the bigger ones, such as standard Belarusian or standard Russian. 

The implemented morphological taggers of standard Russian and standard Belarusian demonstrate an accuracy higher than the accuracy of multilingual models by 3 to 15%. The study suggests possible ways to adapt these taggers to the Khislavichi dataset, such as tagset unification and transcription closer to the actual
sound rather than the standard lect pronunciation. Automatic classification supports the hypothesis that Khislavichi is a border East Slavic lect that historically was Belarusian but got russified: the algorithm places it either slightly closer to Russian or to Belarusian.

The Use of Khislavichi Lect Morphological Tagging to Determine its Position in the East Slavic Group

We introduce DiatopIt, the first corpus specifically focused on diatopic language variation in Italy for language varieties other than Standard Italian. DiatopIt comprises over 15K geolocated social media posts from Twitter over a period of two years, including regional Italian usage and content fully written in local language varieties or exhibiting code-switching with Standard Italian. We detail how we tackled key challenges in creating such a resource, including the absence of orthography standards for most local language varieties and the lack of reliable language identification tools. We assess the representativeness of DiatopIt across time and space, and show that the density of non-Standard Italian content across areas correlates with actual language use. We finally conduct computational experiments and find that modeling diatopic variation on highly multilingual areas such as Italy is a complex task even for recent language models.

DiatopIt: A Corpus of Social Media Posts for the Study of Diatopic Language Vari- ation in Italy

Language label tokens are often used in multilingual neural language modeling and sequence-to-sequence learning to enhance the performance of such models. An additional product of the technique is that the models learn representations of the language tokens, which in turn reflect the relationships between the languages. In this paper, we study the learned representations of dialects produced by neural dialect-to-standard normalization models. We use two large datasets of typologically different languages, namely Finnish and Norwegian, and evaluate the learned representations against traditional dialect divisions of both languages. We find that the inferred dialect embeddings correlate well with the traditional dialects. The methodology could be further used in noisier settings to find new insights into language variation.

Dialect Representation Learning with Neural Dialect-to-Standard Normalization

This report describes first an industrial use case for identifying closely related languages, e.g. dialects, namely the detection of languages of movie subtitle documents. We then present a 2-stage architecture that is able to detect macrolanguages in the first stage and language variants in the second. Using our architecture, we participated in the DSL-TL Shared Task of the VarDial 2023 workshop. We describe the results of our experiments. In the first experiment we report an accuracy of 97.8% on a set of 460 subtitle files. In our second experiment
we used DSL-TL data and achieve a macro-average F1 of 76% for the binary task, and 54% for the three-way task in the dev set. In the open track, we augment the data with named entities retrieved from Wikidata and achieve minor increases of about 1% for both tracks.

VarDial in the Wild: Industrial Applications of LID Systems for Closely-Related Language Varieties

The present work describes the solutions proposed by the UnibucNLP team to address the closed format of the DSL-TL task featured in the tenth VarDial Evaluation Campaign. The DSL-TL organizers provided approximately 11 thousand sentences written in three different languages and manually tagged with one of 9
classes. Out of these, 3 tags are considered common label and the remaining 6 tags are variety-specific. The DSL-TL task features 2 subtasks: Track 1 - a three-way and Track 2 - a two-way classification per language. In Track 2 only the variety-specific labels are used for scoring, whereas in Track 1 the common label is considered as well. Our team participated in both tracks, with three ensemble-based submissions for each. The meta-learner used for Track 1 is XGBoost and for Track 2, Logistic Regression. With each submission, we are gradually increasing the complexity of the ensemble, starting with two shallow, string-kernel based methods. To the first ensemble, we add a convolutional neural network for our second submission. The third ensemble submitted adds a fine-tuned BERT model to the second one. In
Track 1, ensemble three is our highest ranked, with an F 1 − score of 53.18%; 5.36% less than the leader. Surprisingly, in Track 2 the ensemble of shallow methods surpasses the other two, more complex ensembles, achieving an F 1 − score of 69.35%

Using Ensemble Learning in Language Variety Identification

SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties

In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated 
comment moderation systems greatly aid human moderators by either automatically 
classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL1 – a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge
are required to interpret the comment.

CoRAL: a Context-aware Croatian Abusive Language Dataset

Language embeds information about social, cultural, and political values people hold. Prior work has explored potentially harmful social biases encoded in Pre-trained Language Models (PLMs). However, there has been no systematic study investigating how values embedded in these models vary across cultures.
In this paper, we introduce probes to study which cross-cultural values are embedded in these models, and whether they align with existing theories and cross-cultural values surveys. We find that PLMs capture differences in values across cultures, but those only weakly align with established values surveys. We discuss implications of using mis-aligned models in cross-cultural settings, as well as ways of aligning PLMs with values surveys.

Probing Pre-Trained Language Models for Cross-Cultural Differences in Values

As the global crisis of language endangerment deepens, Indigenous communities have continued to seek new means of preserving, promoting and passing on their languages to future generations. For many communities, modern language technology holds the promise of accelerating that process. However, the cultural and disciplinary divides between documentary linguists, computational linguists and Indigenous communities have posed an on-going challenge for the development and deployment of NLP applications that can support the documentation and revitalization of Indigenous languages. In this paper, we discuss the main barriers to collaboration that these groups have encountered, as well as some notable initiatives in recent years to bring the groups closer together. We follow this with specific recommendations to build upon those efforts, calling for increased opportunities for awareness-building and skills-training in computational linguistics, tailored to the specific needs of both documentary linguists and Indigenous community members. We see this as an essential step as we move forward into an era of NLP-assisted language revitalization.

Strengthening Relationships Between Indigenous Communities, Documentary Linguists, and Computational Linguists in the Era of NLP-Assisted Language Revitalization

Increasingly, language models and machine translation are becoming valuable tools to help people communicate with others from diverse cultural backgrounds. However, current language models lack cultural awareness because they are trained on data representing only the culture within the dataset. This presents a problem in hate speech classification, where cultural awareness is especially critical. This study aims to quantify the cultural insensitivity of three monolingual (Korean, English, Arabic) hate speech classifiers by evaluating their performance on translated datasets from the other two languages. Our research has revealed that hate speech classifiers evaluated on datasets from different cultures yield significantly lower F1 scores, up to almost 50%. In addition, they produce considerably higher false negative rates, with a magnitude up to five times greater, demonstrating the extent of the cultural gap. The study highlights the severity of cultural insensitivity of language models in hate speech classification.

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The Use of Khislavichi Lect Morphological Tagging to Determine its Position in the East Slavic Group

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Changing usage of Low Saxon auxiliary and modal verbs