Multiword expressions (MWEs) have been a major challenge in NLP for decades and research on MWEs was driven notably by shared tasks, including those organized by the PARSEME community. We report the organisation and the results of edition 2.0 of the PARSEME shared task. For the first time, all syntactic categories are covered: verbal, nominal, adjectival, adverbial and functional. We rely on edition 2.0 of the PARSEME corpus, annotated for all these categories in 17 languages. We create a new dataset with paraphrases of sentences containing idioms in 14 languages, and defining a new subtask dedicated to MWE paraphrasing. We extend our evaluation protocol by measuring both performance and diversity of systems, and including manual evaluation in paraphrasing. 10 systems, including the baseline, participated in the MWE identification subtask and 5 in the paraphrasing subtask. Results are promising, but known MWE identification challenges remain unsolved. Performance correlates positively with diversity in MWE identification, and negatively in MWE paraphrasing.

We present edition 2.0 of the PARSEME multilingual corpus annotated for multiword expressions (MWEs), resulting from efforts of the PARSEME community towards universality-driven modeling of idiomaticity. With respect to previous editions, we extend the annotation scope to all syntactic MWE categories: verbal, nominal, adjectival, adverbial and functional. We cover 17 languages, of which 7 are new. The annotation process is based on cross-lingually unified guidelines, phrased as decision diagrams over linguistic tests, and a typology of 18 MWE categories. The corpus contains almost 5 million tokens, over 250,000 sentences and 140,000 MWE annotations. The applicability of the corpus is tested in baseline experiments with a prompt-based MWE identification system. Results show that generic large language models do not encode sufficient knowledge to solve the MWE identification task.

Le démonstrateur que nous décrivons ici est un prototype de système de dialogue dont l’objectif est de simuler un patient. Nous décrivons son fonctionnement général en insistant sur les aspects concernant la langue et surtout le rapport entre langue médicale de spécialité et langue générale.

This paper is concerned with human assessments of the severity of errors in ASR outputs. We did not design any guidelines so that each annotator involved in the study could consider the “seriousness” of an ASR error using their own scientific background. Eight human annotators were involved in an annotation task on three distinct corpora, one of the corpora being annotated twice, hiding this annotation in duplicate to the annotators. None of the computed results (inter-annotator agreement, edit distance, majority annotation) allow any strong correlation between the considered criteria and the level of seriousness to be shown, which underlines the difficulty for a human to determine whether a ASR error is serious or not.

In the present contribution we start with an overview of the linguistic situation of Luxembourg. We then describe specificities of spoken and written Lëtzebuergesch, with respect to automatic speech processing. Multilingual code-switching and code-mixing, poor writing standardization as compared to languages such as English or French, a large diversity of spoken varieties, together with a limited written production of Lëtzebuergesch language contribute to pose many interesting challenges to automatic speech processing both for speech technologies and linguistic studies. Multilingual filtering has been investigated to sort out Luxembourgish from German and French. Word list coverage and language model perplexity results, using sibling resources collected from the Web, are presented. A phonemic inventory has been adopted for pronunciation dictionary development, a grapheme-phoneme tool has been developed and pronunciation research issues related to the multilingual context are highlighted. Results achieved in resource development allow to envision the realisation of an ASR system.

The RITEL project aims to integrate a spoken language dialogue system and an open-domain information retrieval system in order to enable human users to ask a general question and to refine their search for information interactively. This type of system is often referred to as an Interactive Question Answering (IQA) system. In this paper, we present an evaluation of how the performance of the RITEL system differs when users interact with it using spoken versus textual input and output. Our results indicate that while users do not perceive the two versions to perform significantly differently, many more questions are asked in a typical text-based dialogue.