no code implementations • EMNLP (sustainlp) 2020 • Ariadna Quattoni, Xavier Carreras
We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA).
no code implementations • Findings (EMNLP) 2021 • Ariadna Quattoni, Xavier Carreras
We address the annotation data bottleneck for sequence classification.
no code implementations • 24 Oct 2022 • Francesco Cazzaro, Ariadna Quattoni, Xavier Carreras
We focus on sparse sequence classification, that is problems in which the target class is rare and compare three deep learning sequence classification models.
1 code implementation • 10 Oct 2022 • Francesco Cazzaro, Davide Locatelli, Ariadna Quattoni, Xavier Carreras
Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks.
no code implementations • ACL 2019 • Ariadna Quattoni, Xavier Carreras
Spectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties.
no code implementations • COLING 2018 • Joana Ribeiro, Shashi Narayan, Shay B. Cohen, Xavier Carreras
We show that the general problem of string transduction can be reduced to the problem of sequence labeling.
no code implementations • WS 2017 • Miguel Ballesteros, Xavier Carreras
We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees.
no code implementations • WS 2017 • Pranava Swaroop Madhyastha, Xavier Carreras, Ariadna Quattoni
We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task).
no code implementations • 9 Jun 2017 • Ariadna Quattoni, Xavier Carreras, Matthias Gallé
Spectral algorithms reduce the learning problem to the task of computing an SVD decomposition over a special type of matrix called the Hankel matrix.
no code implementations • 22 Dec 2014 • Pranava Swaroop Madhyastha, Xavier Carreras, Ariadna Quattoni
We investigate the problem of inducing word embeddings that are tailored for a particular bilexical relation.
no code implementations • LREC 2014 • Llu{\'\i}s Padr{\'o}, {\v{Z}}eljko Agi{\'c}, Xavier Carreras, Blaz Fortuna, Esteban Garc{\'\i}a-Cuesta, Zhixing Li, Tadej {\v{S}}tajner, Marko Tadi{\'c}
This paper presents the linguistic analysis tools and its infrastructure developed within the XLike project.
no code implementations • NeurIPS 2013 • Raphael Bailly, Xavier Carreras, Ariadna Quattoni
Finite-State Transducers (FST) are a standard tool for modeling paired input-output sequences and are used in numerous applications, ranging from computational biology to natural language processing.
no code implementations • TACL 2013 • Xavier Llu{\'\i}s, Xavier Carreras, Llu{\'\i}s M{\`a}rquez
For the syntactic part, we define a standard arc-factored dependency model that predicts the full syntactic tree.