Croatia

EACL 2023

You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models

multilinguality

language modeling

knowledge distillation

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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.

We present a large, multilingual study into how vision constrains linguistic choice, covering four languages and five linguistic properties, such as verb transitivity or use of numerals. 
We propose a novel method that leverages existing corpora of images with captions written by native speakers, and apply it to nine corpora, comprising 600k images and 3M captions. 
We study the relation between visual input and linguistic choices by training classifiers to predict the probability of expressing a property from raw images, and find evidence supporting the claim that linguistic properties are constrained by visual context across languages. 
We complement this investigation with a corpus study, taking the test case of numerals. Specifically, we use existing annotations (number or type of objects) to investigate the effect of different visual conditions on the use of numeral expressions in captions, and show that similar patterns emerge across languages.
Our methods and findings both confirm and extend existing research in the cognitive literature. We additionally discuss possible applications for language generation.

A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions

We introduce a crosslinguistic database for attitude predicates, which references their combinatorial (syntactic) and semantic properties. Our data allows assessment of crosslinguistic generalizations about attitude predicates as well as discovery of new typological/crosslinguistic patterns. This presentation highlights empirical and theoretical issues that our database will help to address, motivates the predicate sample and the properties that it references, as well as our methodological choices. Two case studies illustrate how the database can be used to assess the validity of crosslinguistic generalizations.

A Crosslinguistic Database for Combinatorial and Semantic Properties of Attitude Predicates

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.

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

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.

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