Croatia

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.

EACL 2023

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

idiomaticity

semantic

semantics

biomedical

clinical

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.

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.

The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative

Pre-trained neural masked language models are often used for predicting a replacement token for a given sequence position, in a cloze-like task. However, this usage is restricted to predicting a single token, from a relatively small pre-trained vocabulary. Recent Sequence2Sequence pre-trained LMs like T5 do allow predicting multi-token completions, but are more expensive to train and run. We show that pre-trained masked language models can be adapted to produce multi-token completions, with only a modest addition to their parameter count. We propose two simple adap- tation approaches, trading parameter counts for accuracy. The first method generates multi- token completions from a conditioned RNN. It has a very low parameter count and achieves competitive results. The second method is even simpler: it adds items corresponding to multi-token units to the output prediction ma- trix. While being higher in parameter count than the RNN method, it also surpasses current state-of-the-art multi-token completion mod- els, including T5-3B, while being significantly more parameter efficient. We demonstrate that our approach is flexible to different vocabularies and domains and can effectively leverage existing pre-trained models available in different domains. Finally, a human evaluation further validates our results and shows that our solution regularly provides valid completions, as well as reasonable correctness for factual-sentence completions.

Simple and Effective Multi-Token Completion from Masked Language Models

One of the major challenges that underrepresented and endangered language communities face in language technology is the lack or paucity of language data. This is also the case of the Southern varieties of the Kurdish and Laki languages for which very limited resources are available with insubstantial progress in tools. To tackle this, we provide a few approaches that rely on the content of local news websites, a local radio station that broadcasts content in Southern Kurdish and fieldwork for Laki. In this paper, we describe some of the challenges of such under-represented
languages, particularly in writing and standardization, and also, in retrieving sources of data and retro-digitizing handwritten content to create a corpus for Southern Kurdish and Laki. In addition, we study the task of language identification in light of the other variants of Kurdish and Zaza-Gorani languages.

Approaches to Corpus Creation for Low-Resource Language Technology: the Case of Southern Kurdish and Laki

Experiments to fine-tune large multilingual models with limited data from a specific domain or setting has potential to improve automatic speech recognition (ASR) outcomes. This paper reports on the use of the Elpis ASR pipeline to fine-tune two pre-trained base models, Wav2Vec2-XLSR-53 and Wav2Vec2- Large-XLSR-Indonesian, with various mixes of data from 3 YouTube channels teaching Indonesian with English as the language of instruction. We discuss our results inferring new lesson audio (22-46% word error rate) in the context of speeding data collection in diverse and specialised settings. This study is an example of how ASR can be used to accelerate natural language research, expanding ethically sourced data in low-resource settings.

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

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

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