Croatia

This paper applies the ontology-based
dialectometric technique of Engsterhold
(2020) to surnames. The method was
originally developed for phonetic analyses.
However, as will be shown, it is also suited
for the study of graphemic representations.
Based on data from the German Surname
Atlas (DFA), the method is optimized for
graphemic analysis and illustrated with an
example case.

EACL 2023

Reconstructing Language History by Using a Phonological Ontology. An Analysis of German Surnames

surnames

dialectometry

ontology

german

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.

Automatic discrimination between Bosnian, Croatian, Montenegrin and Serbian is a hard task due to the mutual intelligibility of these South-Slavic languages. In this paper, we introduce the BENCHić-lang benchmark for discriminating between these four languages. The benchmark consists of two datasets from different domains -- a Twitter and a news dataset -- selected with the aim of fostering cross-dataset evaluation of different modelling approaches. We experiment with the baseline SVM models, based on character n-grams, which perform nicely in-dataset, but do not generalize well in cross-dataset experiments. Thus, we introduce another approach, exploiting only web-crawled data and the weak supervision signal coming from the respective country/language top-level domains. The resulting simple Naive Bayes model, based on less than a thousand word features extracted from web data, outperforms the baseline models in the cross-dataset scenario and achieves good levels of generalization across datasets.

BENCHic ́-lang: A Benchmark for Discriminating between Bosnian, Croatian, Mon- tenegrin and Serbian

Based on historical dialect data we introduce a local measure of linguistic coherence in spatial language variation aiming at the identification of regions which are particularly sensitive to language variation and change. Besides, we use a measure of global coherence for the automated detection of linguistic items (e.g., sounds or morphemes) with higher or lesser language variation. The paper describes both the data and the method and provides analyses examples.

A Measure for Linguistic Coherence in Spatial Language Variation

We examine whether large neural language models, trained on very large collections of varied English text, learn the potentially long-distance dependency of British versus American spelling conventions, i.e., whether spelling is consistently one or the other within model-generated strings. In contrast to non-surface (e.g., syntactic) long-distance dependencies, spelling consistency is easy to measure in both LMs and the typically quite noisy source training data, which provides additional insight into observed model behavior. We first demonstrate that the training data does exhibit some degree of consistency, then that one of the two examined models (GPT2) shows little sensitivity to this dependency. The other model (T5) does capture some degree of consistency, however fails to generalize robustly to nonce words. Those looking to rely up such models to generate text that is spelled consistently with one convention (e.g., in response generation or keyboard next word prediction) likely still require some kind of post-processing to guarantee consistency.

Spelling convention sensitivity in neural language models

We argue that dialect identification should be treated as a multi-label classification problem rather than the single-class setting prevalent in existing collections and evaluations. In order to avoid extensive human re-labelling of the data, we propose an analysis of ambiguous near-duplicates in an existing collection covering four variants of French. We show how this analysis helps us provide multiple labels for a significant subset of the original data, therefore enriching the annotation with minimal human intervention. The resulting data can then be used to train dialect identifiers in a multi-label setting. Experimental results show that on the enriched dataset, the multi-label classifier produces similar accuracy to the single-label classifier on test cases that are unambiguous (single label), but it increases the macro-averaged F1-score by 0.225 absolute (71% relative gain) on ambiguous texts with multiple labels. On the original data, gains on the ambiguous test cases are smaller but still considerable (0.077 absolute, 20% relative), and accuracy on non-ambiguous test cases is again similar in this case. This supports our thesis that modelling dialect identification as a multi-label problem potentially has a positive impact.

Dialect and Variant Identification as a Multi-Label Classification Task: A Proposal Based on Near-Duplicate Analysis

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.

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

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%

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Reconstructing Language History by Using a Phonological Ontology. An Analysis of German Surnames

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BENCHic ́-lang: A Benchmark for Discriminating between Bosnian, Croatian, Mon- tenegrin and Serbian

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