Croatia

In this replication study of previous research into dependency length minimisation (DLM), we pilot a new parallel multilingual parsed corpus to examine whether previous findings are upheld when controlling for variation in domain and sentence content between languages.
We follow the approach of previous research in comparing the dependency lengths of observed sentences in a multilingual corpus to a variety of baselines: permutations of the sentences, either random or according to some fixed schema.
We go on to compare DLM with intervener complexity measure (ICM), an alternative measure of syntactic complexity.
Our findings uphold both dependency length and intervener complexity minimisation in all languages under investigation. We also find a markedly lesser extent of dependency length minimisation in verb-final languages, and the same for intervener complexity measure.
We conclude that dependency length and intervener complexity minimisation as universals are upheld when controlling for domain and content variation, but that further research is needed into the asymmetry between verb-final and other languages in this regard.

EACL 2023

Revisiting Dependency Length and Intervener Complexity Minimisation on a Parallel Corpus in 35 Languages

dependency length minimisation

typology

corpus

cross-lingual

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference. 
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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 paper describes the structure and findings of the SIGTYP 2023 shared task on cognate and derivative detection for low-resourced languages, broken down into a supervised and unsupervised sub-task.
The participants were asked to submit the test data's final prediction.
A total of nine teams registered for the shared task, where seven teams registered for both sub-tasks. Only two participants ended up submitting system descriptions, with only one submitting systems for both sub-tasks.
While all systems show a rather promising performance, all could be within the baseline score for the supervised sub-task. However, the system submitted for the unsupervised sub-task outperforms the baseline score.

Findings of the SIGTYP 2023 Shared task on Cognate and Derivative DetectionFor Low-Resourced Languages

ÚFAL Submission for SIGTYP Supervised Cognate Detection Task

Of approximately 7,000 languages around the world, only a handful have abundant computational resources. Extending the reach of language technologies to diverse, less-resourced languages is important for tackling the challenges of digital equity and inclusion. Here we introduce the Grambank typological database as a resource to support such efforts. To date, work that uses typological data to extend computational research to less-resourced languages has relied on cross-linguistic morphosyntax datasets that are sparsely populated, use categorical coding that can be difficult to interpret, and introduce redundant information across features. Grambank presents similar information (e.g. word order, grammatical relation marking, constructions like interrogatives and negation), but is designed to avoid several disadvantages of legacy typological resources. Grambank’s 195 features encode basic information about morphology and syntax for over 2,400 languages. 83% of these languages are annotated for at least 100 features. By implementing binary coding for most features and curating the dataset to avoid logical dependencies, Grambank presents information in a user-friendly format for computational applications. The scale, completeness, reliability, format, and documentation of Grambank make it a useful resource for linguistically-informed models, cross-lingual NLP, and research targeting less-resourced languages.

Grambank’s typological advances support work on less-resourced languages

Language-agnostic measures discriminate inflection and derivation

Does Topological Ordering of Morphological Segments Reduce Morphological Modeling Complexity? A Preliminary Study on 13 Languages

In this paper we test the parsing performances of a multilingual parser on Old English data using different sets of languages, alone and combined with the target language, to train the models. We compare the results obtained by the models and we analyze more in deep the annotation of some peculiar syntactic constructions of the target language, providing plausible linguistic explanations of the errors made even by the best performing models.

Using modern languages to parse ancient ones: a test on Old English

We present a comparative analysis of four different syntactic typological approaches applied to 20 different languages to determine the most effective one to be used for the improvement of dependency parsing results via corpora combination. We evaluated these strategies by calculating the correlation between the language distances and the empirical LAS results obtained when languages were combined in pairs. From the results, it was possible to observe that the best method is based on the extraction of word order patterns which happen inside subtrees of the syntactic structure of the sentences.

Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement

Machine learning drives forward the development in many areas of Natural Language Processing (NLP). Until now, many NLP systems and research are focusing on high-resource languages, i.e. languages for which many data resources exist. Recently, so-called low-resource languages increasingly come into focus. In this context, multi-lingual language models, which are trained on related languages to a target low-resource language, may enable NLP tasks on this low-resource language. In this work, we investigate the use of multi-lingual models for Named Entity Recognition (NER) for low-resource languages. We consider the West Slavic language family and the low-resource languages Upper Sorbian and Kashubian. Three RoBERTa models were trained from scratch, two mono-lingual models for
Czech and Polish, and one bi-lingual model for Czech and Polish. These models were
evaluated on the NER downstream task for Czech, Polish, Upper Sorbian, and Kashubian, and compared to existing state-of-the-art models such as RobeCzech, HerBERT, and XLM-R. The results indicate that the mono-lingual models perform better on the language they were trained on, and both the mono-lingual and
language family models outperform the large multi-lingual model in downstream tasks. Overall, the study shows that low-resource West Slavic languages can benefit from closely related languages and their models.


Named Entity Recognition for Low-Resource Languages – Profiting from Language Families

Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.

MAUPQA: Massive Automatically-created Polish Question Answering Dataset

Pre-trained Transformer-based models have become immensely popular amongst NLP practitioners. We present TrelBERT -- the first Polish language model suited for application in the social media domain. TrelBERT is based on an existing general-domain model and adapted to the language of social media by pre-training it further on a large collection of Twitter data. We demonstrate its usefulness by evaluating it in the downstream task of cyberbullying detection, in which it achieves state-of-the-art results, outperforming larger monolingual models trained on general-domain corpora, as well as multilingual in-domain models, by a large margin. We make the model publicly available. We also release a new dataset for the problem of harmful speech detection.

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Findings of the SIGTYP 2023 Shared task on Cognate and Derivative DetectionFor Low-Resourced Languages

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