1 citations · 2 across the 5 of their papers we have counts for
10 papers
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss
Wei He, Marco Idiart, Carolina Scarton +1
Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP). This is partly because these expressions do not…
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
Kun Zhao, Bohao Yang, Chenghua Lin +3
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which…
Assessing Linguistic Generalisation in Language Models: A Dataset for Brazilian Portuguese
Rodrigo Wilkens, Leonardo Zilio, Aline Villavicencio
Much recent effort has been devoted to creating large-scale language models. Nowadays, the most prominent approaches are based on deep neural networks, such as BERT. However, they…
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report
Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella +3
We provide a summary of the fifth edition of the CASE workshop that is held in the scope of EMNLP 2022. The workshop consists of regular papers, two keynotes, working papers of sha…
Why So Down? The Role of Negative (and Positive) Pointwise Mutual Information in Distributional Semantics
Alexandre Salle, Aline Villavicencio
In distributional semantics, the pointwise mutual information () weighting of the cooccurrence matrix performs far better than raw counts. There is, however, an issue…
Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
Marcely Zanon Boito, Aline Villavicencio, Laurent Besacier
Since Bahdanau et al. [1] first introduced attention for neural machine translation, most sequence-to-sequence models made use of attention mechanisms [2, 3, 4]. While they produce…