1 code implementation • 1 Apr 2022 • Imke Grabe, Jichen Zhu, Manex Agirrezabal
This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles.
1 code implementation • 24 May 2022 • Aitor Ormazabal, Mikel Artetxe, Manex Agirrezabal, Aitor Soroa, Eneko Agirre
During inference, we build control codes for the desired meter and rhyme scheme, and condition our language model on them to generate formal verse poetry.
1 code implementation • ACL 2022 • Sidsel Boldsen, Manex Agirrezabal, Nora Hollenstein
Character-level information is included in many NLP models, but evaluating the information encoded in character representations is an open issue.
1 code implementation • 31 Oct 2023 • Manex Agirrezabal, Hugo Gonçalo Oliveira, Aitor Ormazabal
We present Erato, a framework designed to facilitate the automated evaluation of poetry, including that generated by poetry generation systems.
no code implementations • RANLP 2017 • Manex Agirrezabal, Iñaki Alegria, Mans Hulden
Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science.
no code implementations • WS 2016 • Pablo Gamallo, I{\~n}aki Alegria, Jos{\'e} Ramom Pichel, Manex Agirrezabal
This article describes the systems submitted by the Citius{\_}Ixa{\_}Imaxin team to the Discriminating Similar Languages Shared Task 2016.
Automatic Speech Recognition (ASR) General Classification +2
no code implementations • COLING 2016 • Manex Agirrezabal, I{\~n}aki Alegria, Mans Hulden
In this work we tackle the challenge of identifying rhythmic patterns in poetry written in English.
no code implementations • WS 2019 • Sidsel Boldsen, Manex Agirrezabal, Patrizia Paggio
In this work we propose a data-driven methodology for identifying temporal trends in a corpus of medieval charters.
no code implementations • LREC 2020 • Patrizia Paggio, Manex Agirrezabal, Bart Jongejan, Costanza Navarretta
This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers.
no code implementations • WS 2020 • Manex Agirrezabal, J{\"u}rgen Wedekind
We present a model for the unsupervised dis- covery of morphological paradigms.
no code implementations • NAACL (TeachingNLP) 2021 • Manex Agirrezabal
In this article, we show and discuss our experience in applying the flipped classroom method for teaching Conditional Random Fields in a Natural Language Processing course.
no code implementations • EMNLP (LAW, DMR) 2021 • Patrizia Paggio, Costanza Navarretta, Bart Jongejan, Manex Agirrezabal
We present a method to support the annotation of head movements in video-recorded conversations.
no code implementations • WS (NoDaLiDa) 2019 • Sidsel Boldsen, Manex Agirrezabal
The use of a linking element between compound members is a common phenomenon in Germanic languages.
no code implementations • ACL (SIGMORPHON) 2021 • Adam Wiemerslage, Arya D. McCarthy, Alexander Erdmann, Garrett Nicolai, Manex Agirrezabal, Miikka Silfverberg, Mans Hulden, Katharina Kann
We describe the second SIGMORPHON shared task on unsupervised morphology: the goal of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering is to cluster word types from a raw text corpus into paradigms.
no code implementations • GWC 2018 • Bolette Pedersen, Manex Agirrezabal, Sanni Nimb, Ida Olsen, Sussi Olsen
Our aim is to develop principled methods for sense clustering which can make existing lexical resources practically useful in NLP – not too fine-grained to be operational and yet finegrained enough to be worth the trouble.
no code implementations • 19 Mar 2022 • Janek Amann, Manex Agirrezabal
In this paper we propose the use of the Word2vec algorithm in order to obtain odor perception embeddings (or smell embeddings), only using publicly available perfume descriptions.
no code implementations • LTEDI (ACL) 2022 • Manex Agirrezabal, Janek Amann
In this paper we present our approach for detecting signs of depression from social media text.
no code implementations • 15 Jun 2023 • Manex Agirrezabal
In this paper we present our method for tasks 2 and 3A at the CheckThat2023 shared task.