Selected publications of the language technologies lab


Preprints and technical reports

  • LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain

    Joel Niklaus, Veton Matoshi, Pooja Rani, Andrea Galassi, Matthias Stürmer, Ilias Chalkidis
    January 2023

  • Investigating Logic Tensor Networks for Neural-Symbolic Argument Mining

    Andrea Galassi, Marco Lippi, Paolo Torroni
    October 2021

  • BANANA: a Benchmark for the Assessment of Neural Architectures for Nucleic Acids

    Luca Salvatore Lorello, Andrea Galassi, Paolo Torroni
    September 2021

  • MemBERT: Injecting Unstructured Knowledge into BERT

    Transformers changed modern NLP in many ways. However, they can hardly exploit domain knowledge, and like other blackbox models, they lack interpretability. Unfortunately, structured knowledge injection, in the long run, risks to suffer from a knowledge acquisition bottleneck. We thus propose a memory enhancement of transformer models that makes use of unstructured domain knowledge expressed in plain natural language. An experimental evaluation conducted on two challenging NLP tasks demonstrates that our approach yields better performance and model interpretability than baseline transformer-based architectures.

  • Tree-constrained Graph Neural Networks for Argument Mining

    We propose a novel architecture for Graph Neural Networks that is inspired by the idea behind Tree Kernels of measuring similarity between trees by taking into account their common substructures, named fragments. By imposing a series of regularization constraints to the learning problem, we exploit a pooling mechanism that incorporates such notion of fragments within the node soft assignment function that produces the embeddings. We present an extensive experimental evaluation on a collection of sentence classification tasks conducted on several argument mining corpora, showing that the proposed approach performs well with respect to state-of-the-art techniques.