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The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022.After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown. Although the conference was held in dual mode, we strongly suggested the participants to attend it coming to Milan. Indeed, we received a strong feedback on this aspect from the community, which was eager to meet in person and enjoy both the scientific and social events together with the colleagues. In total, 99 participants registered to the conference benefiting from the early registration fee, 91 out of which expressed their intention to attend the event in person, which we consider as a very positive indication of enthusiasm from the community, given the uncertain situation due to the evolution of the pandemic in Italy.In total, we received 68 proposals, organized in the following specific tracks: Information Extraction,

Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models. In this way, avoiding using intermediate steps, i.e. automatic speech recognition, is possible, thus the propagation of errors due to background noise, spontaneous speech, speaking styles of users, etc. Towards the development of solutions applicable in real scenarios, it is interesting to investigate how environmental noise and related noise reduction techniques to address the intent classification task with end-to-end neural models.In this paper, we experiment with a noisy version of the fluent speech command data set, combining the intent classifier with a time-domain speech enhancement solution based on Wave-U-Net and considering different training strategies. Experimental results reveal that, for this task, the use of speech enhancement greatly improves the classification accuracy in noisy conditions, in particular when the classification model is trained on enhanced signals.

Intelligent Computer-Assisted Language Learning (ICALL) aims to design effective systems for the analysis of learners’ production in a target language ensuring both successful learning and motivated learners. Most of the existing systems, however, focus extensively on the form rather than on the meaning of language. To create effective systems facilitating personalized language learning both form and meaning should be considered. The reason behind this is that language is a continuous flow of information passing from one user or agent to another, both during comprehension and production. This becomes even more relevant in the case of second or foreign languages (L2), where certain linguistic choices may be dictated by inexact form-meaning links construed by the learner. In this research project, we focus on the analysis of the spoken production of adult learners of German, taking argument and information structure as a use case. We use Fluid Construction Grammar as a formalism, which captures relevant linguistic aspects at both the syntactic level (form) and the semantic level (meaning). Its particularity lies in the possibility of closely monitoring bidirectional form-meaning interactions starting from constructions of different nature modeled in an extensively customizable way. Our work is in progress, and we focus on ways to provide helpful feedback on meaning. German displays a rather articulated grammar and obtaining insights not only on its formal but also on its semantic correctness could offer important steps forward for Intelligent CALL. The design of computational systems for Intelligent CALL that can effectively support L2 learners in personalized learning requires a grammatical framework that is computationally effective and offers linguistic and acquisitional perspicuity (Schulze & Penner, 2008).

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