CLiC-it Best Student Paper

AILC awards a prize for the best “student paper” with the aim of stimulating and recognizing works presented at the CLiC-it annual conference by young researchers.

The prize is awarded to articles that meet the following criteria: (i) the first author appears as a doctoral or master’s degree student on the date of submission and (ii) the article is presented at the conference by the student himself.

Entries are evaluated by a three-member jury, including at least one of the organizers (co-chair) of the CLiC-it conference. The choice of the award winner is unanimous.

The authors of the award-winning article are invited to submit an extended version of their work to IJCoL (Italian Journal of Computational Linguistics).

List of winners:

  • 2023: Andrea Santilli, Emanuele Rodolà. Camoscio. An Italian Instruction-tuned LLaMA.
    Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, Dario Balestri. Contrastive Language–Image Pre-training for the Italian
  • 2021: Tolulope Ògúnrẹ̀mí, Nazanin Sabri, Valerio Basile and Tommaso Caselli. Leveraging Bias in Pre-trained Word Embeddings for Unsupervised Microaggression Detection.
  • 2020: Marco Gaido, Mattia Antonino Di Gangi, Matteo Negri, Marco Turchi. On Knowledge Distillation for Direct Speech Translation.
  • 2019: Simon Preissner, Aurelie Herbelot. To Be Fair: A Case for Cognitively-Inspired Models of Meaning.
  • 2018: Danilo Croce, Daniele Rossini, Roberto Basili. On the Readability of Deep Learning Models: the role of Kernel-based Deep Architectures.
  • 2017: Ludovica Pannitto, Lavinia Salicchi, Alessandro Lenci. AHyDA: Automatic Hypernym Detection with feature Augmentation.
  • 2016: Edoardo Maria Ponti, Elisabetta Jezek, Bernardo Magnini. Grounding the Lexical Sets of Causative-Inchoative Verbs with Word Embedding.
  • 2015: Daniele Bonadiman, Aliaksei Severyn, Alessandro Moschitti. Deep Neural Networks for Named Entity Recognition in Italian.
  • 2014: Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro. Analysing Word Meaning over Time by Exploiting Temporal Random Indexing.