Croatia

We present version 1.3 of the PARSEME multilingual corpus annotated with verbal multiword expressions. Since the previous version, new languages have joined the undertaking of creating such a resource, some of the already existing corpora have been enriched with new annotated texts, while others have been enhanced in various ways. This fourth release (v.1.3) is the first one to cover the union of all the languages included in the previous three releases. The PARSEME multilingual corpus represents 26 languages now. All monolingual corpora therein use Universal Dependencies v.2 tagset. They are (re-)split observing the PARSEME v.1.2 standard, which puts an impact on unseen VMWEs. With the current iteration, the corpus release process has been detached from shared tasks; instead, a process for continuous improvement and systematic releases has been introduced.

EACL 2023

PARSEME corpus release 1.3

parseme shared task.

corpus query

verbal multiword expressions

corpus annotation

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.

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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 identification of Verbal Multiword Expressions (VMWEs) presents a greater challenge compared to non-verbal MWEs due to their higher surface variability. VMWEs are linguistic units that exhibit varying levels of semantic opaqueness and pose difficulties for computational models in terms of both their identification and the degree of compositionality. In this study, a new approach to predicting the compositional nature of VMWEs in Persian is presented. The method begins with an automatic identification of VMWEs in Persian sentences, which is approached as a sequence labeling problem for recognizing the components of VMWEs. The method then creates word embeddings that better capture the semantic properties of VMWEs and uses them to determine the degree of compositionality through multiple criteria. The study compares two neural architectures for identification, BiLSTM and ParsBERT, and shows that a fine-tuned BERT model surpasses the BiLSTM model in evaluation metrics with an F1 score of 89%. Next, a word2vec embedding model is trained to capture the semantics of identified VMWEs and is used to estimate their compositionality, resulting in an accuracy of 70.9% as demonstrated by experiments on a collected dataset of expert-annotated compositional and non-compositional VMWEs.

Predicting Compositionality of Verbal Multiword Expressions in Persian

Multiword expressions are a key ingredient for developing large-scale and linguistically sound natural language processing technology. This paper describes our improvements in automatically identifying Romanian multiword expressions on the corpus released for the PARSEME v1.2 shared task. Our approach assumes a multilingual perspective based on the recently introduced lateral inhibition layer and adversarial training to boost the performance of the employed multilingual language models. With the help of these two methods, we improve the F1-score of XLM-RoBERTa by approximately 2.7% on unseen multiword expressions, the main task of the PARSEME 1.2 edition. In addition, our results can be considered SOTA performance, as they outperform the previous results on Romanian obtained by the participants in this competition.

Romanian Multiword Expression Detection Using Multilingual Adversarial Training and Lateral Inhibition

This paper shines a light on the potential of definition-based semantic models for detecting idiomatic and semi-idiomatic multiword expressions (MWEs) in clinical terminology. Our study focuses on biomedical entities defined in the UMLS ontology and aims to help prioritize the translation efforts of these entities. In particular, we develop an effective tool for scoring the idiomaticity of biomedical MWEs based on the degree of similarity between the semantic representations of those MWEs and a weighted average of the representation of their constituents. We achieve this using a biomedical language model trained to produce similar representations for entity names and their definitions, called BioLORD. The importance of this definition-based approach is highlighted by comparing the BioLORD model to two other state-of-the-art biomedical language models based on Transformer: SapBERT and CODER. Our results show that the BioLORD model has a strong ability to identify idiomatic MWEs, not replicated in other models. Our corpus-free idiomaticity estimation helps ontology translators to focus on more challenging MWEs.

Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning

In this paper, we considered new cross-lingual settings for the SemEval 2022 task 2 subtask A and PARSEME 1.2 shared tasks, in which models are evaluated on languages that are not seen during training. Our findings indicate that language models are able to learn information about MWEs and idiomaticity that is not language-specific. Our findings further show that additional training data from other languages can be leveraged to give improvements over monolingual models for identifying MWEs and predicting idiomaticity.

Token-level Identification of Multiword Expressions using Pre-trained Multilingual Language Models

Investigating the Effects of MWE Identification in Structural Topic Modelling

We present a graph-based tool which can be used to explore Verbal Multi-Word Expression (VMWE) annotated in the Parseme project.
The tool can be used for linguistic exploration on the data, for helping the manual annotation process and to search for errors or inconsistencies in the annotations.

Graph-based multi-layer querying in Parseme Corpora

This paper describes the process of annotation of 996 lexical bundles (LB) assigned to 39 different discourse functions in a Spanish academic corpus. The purpose of the annotation is to obtain a new Spanish gold-standard corpus of 1,800,000 words useful for training and evaluating computational models that are capable of identifying automatically LBs for each context in new corpora, as well as for linguistic analysis about the role of LBs in academic discourse. The annotation process revealed that correspondence between LBs and discourse functions is not biunivocal and that the degree of ambiguity is high, so linguists’ contribution has been essential for improving the automatic assignation of tags.

Annotation of lexical bundles with discourse functions in a Spanish academic corpus

We report on work in progress dealing with the
automated generation of pronunciation infor-
mation for English multiword terms (MWTs)
in Wiktionary, combining information avail-
able for their single components. We describe
the issues we were encountering, the build-
ing of an evaluation dataset, and our teaming
with the DBnary resource maintainer. Our
approach shows potential for automatically
adding morphosyntactic and semantic informa-
tion to the components of such MWTs.

Enriching Multiword Terms in Wiktionary with Pronunciation Information

The importance of multiword expressions (MWEs) for language learning is well established. While MWE research has been evaluated on various downstream tasks such as syntactic parsing and machine translation, its applications in computer-assisted language learning has been less explored. This paper investigates the selection of MWEs for graded vocabulary lists. Widely used by language teachers and students, these lists recommend a language acquisition sequence to optimize learning efficiency. We automatically generate these lists using difficulty-graded corpora and MWEs extracted based on semantic compositionality. We evaluate these lists on their ability to facilitate text comprehension for learners. Experimental results show that our proposed method generates higher-quality lists than baselines using collocation measures.

Automatic Generation of Vocabulary Lists with Multiword Expressions

Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central building blocks of language, i.e., linguistic units of different granularity that combine syntax and semantics. As a first step towards assessing the compatibility of CxG with the syntactic and semantic knowledge demonstrated by state-of-the-art pretrained language models (PLMs), we present an investigation of their capability to classify and understand one of the most commonly studied constructions, the English comparative correlative (CC). We conduct experiments examining the classification accuracy of a syntactic probe on the one hand and the models' behaviour in a semantic application task on the other, with BERT, RoBERTa, and DeBERTa as the example PLMs. Our results show that all three investigated PLMs are able to recognise the structure of the CC but fail to use its meaning. While human-like performance of PLMs on many NLP tasks has been alleged, this indicates that PLMs still suffer from substantial shortcomings in central domains of linguistic knowledge.

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Predicting Compositionality of Verbal Multiword Expressions in Persian

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