no code implementations • ACL (WAT) 2021 • Rahul Aralikatte, Héctor Ricardo Murrieta Bello, Miryam de Lhoneux, Daniel Hershcovich, Marcel Bollmann, Anders Søgaard
This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization.
no code implementations • NAACL (AmericasNLP) 2021 • Marcel Bollmann, Rahul Aralikatte, Héctor Murrieta Bello, Daniel Hershcovich, Miryam de Lhoneux, Anders Søgaard
We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios.
1 code implementation • 30 Oct 2023 • Heather Lent, Kushal Tatariya, Raj Dabre, Yiyi Chen, Marcell Fekete, Esther Ploeger, Li Zhou, Hans Erik Heje, Diptesh Kanojia, Paul Belony, Marcel Bollmann, Loïc Grobol, Miryam de Lhoneux, Daniel Hershcovich, Michel DeGraff, Anders Søgaard, Johannes Bjerva
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.
1 code implementation • EACL 2021 • Marcel Bollmann, Anders S{\o}gaard
We evaluate two common conjectures in error analysis of NLP models: (i) Morphology is predictive of errors; and (ii) the importance of morphology increases with the morphological complexity of a language.
1 code implementation • ACL 2020 • Marcel Bollmann, Desmond Elliott
The field of natural language processing is experiencing a period of unprecedented growth, and with it a surge of published papers.
no code implementations • RANLP 2019 • Meriem Beloucif, Ana Valeria Gonzalez, Marcel Bollmann, Anders S{\o}gaard
Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios.
no code implementations • ACL 2019 • Simon Flachs, Marcel Bollmann, Anders S{\o}gaard
Training neural sequence-to-sequence models with simple token-level log-likelihood is now a standard approach to historical text normalization, albeit often outperformed by phrase-based models.
2 code implementations • NAACL 2019 • Marcel Bollmann
There is no consensus on the state-of-the-art approach to historical text normalization.
no code implementations • WS 2019 • Marcel Bollmann, Natalia Korchagina, Anders Søgaard
Historical text normalization often relies on small training datasets.
no code implementations • WS 2018 • Marcel Bollmann, Anders S{\o}gaard, Joachim Bingel
Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models.
no code implementations • ACL 2017 • Marcel Bollmann, Joachim Bingel, Anders S{\o}gaard
Automated processing of historical texts often relies on pre-normalization to modern word forms.
no code implementations • COLING 2016 • Marcel Bollmann, Anders Søgaard
Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data.