Zobrazeno 1 - 10
of 2 466
pro vyhledávání: '"Plöger, A."'
Neural machine translation (NMT) systems amplify lexical biases present in their training data, leading to artificially impoverished language in output translations. These language-level characteristics render automatic translations different from te
Externí odkaz:
http://arxiv.org/abs/2412.08473
Autor:
Romanou, Angelika, Foroutan, Negar, Sotnikova, Anna, Chen, Zeming, Nelaturu, Sree Harsha, Singh, Shivalika, Maheshwary, Rishabh, Altomare, Micol, Haggag, Mohamed A., A, Snegha, Amayuelas, Alfonso, Amirudin, Azril Hafizi, Aryabumi, Viraat, Boiko, Danylo, Chang, Michael, Chim, Jenny, Cohen, Gal, Dalmia, Aditya Kumar, Diress, Abraham, Duwal, Sharad, Dzenhaliou, Daniil, Florez, Daniel Fernando Erazo, Farestam, Fabian, Imperial, Joseph Marvin, Islam, Shayekh Bin, Isotalo, Perttu, Jabbarishiviari, Maral, Karlsson, Börje F., Khalilov, Eldar, Klamm, Christopher, Koto, Fajri, Krzemiński, Dominik, de Melo, Gabriel Adriano, Montariol, Syrielle, Nan, Yiyang, Niklaus, Joel, Novikova, Jekaterina, Ceron, Johan Samir Obando, Paul, Debjit, Ploeger, Esther, Purbey, Jebish, Rajwal, Swati, Ravi, Selvan Sunitha, Rydell, Sara, Santhosh, Roshan, Sharma, Drishti, Skenduli, Marjana Prifti, Moakhar, Arshia Soltani, Moakhar, Bardia Soltani, Tamir, Ran, Tarun, Ayush Kumar, Wasi, Azmine Toushik, Weerasinghe, Thenuka Ovin, Yilmaz, Serhan, Zhang, Mike, Schlag, Imanol, Fadaee, Marzieh, Hooker, Sara, Bosselut, Antoine
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the developmen
Externí odkaz:
http://arxiv.org/abs/2411.19799
Autor:
Tatariya, Kushal, Kulmizev, Artur, Poelman, Wessel, Ploeger, Esther, Bollmann, Marcel, Bjerva, Johannes, Luo, Jiaming, Lent, Heather, de Lhoneux, Miryam
Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in multilingual NLP. In the context of low-resource languages, however, these quality assumptions are increasingly being scrutinised. This pa
Externí odkaz:
http://arxiv.org/abs/2411.05527
Being widespread in human motor behavior, dynamic movements demonstrate higher efficiency and greater capacity to address a broader range of skill domains compared to their quasi-static counterparts. Among the frequently studied dynamic manipulation
Externí odkaz:
http://arxiv.org/abs/2410.19591
Machine translations are found to be lexically poorer than human translations. The loss of lexical diversity through MT poses an issue in the automatic translation of literature, where it matters not only what is written, but also how it is written.
Externí odkaz:
http://arxiv.org/abs/2408.17308
Autor:
Ploeger, Esther, Poelman, Wessel, Høeg-Petersen, Andreas Holck, Schlichtkrull, Anders, de Lhoneux, Miryam, Bjerva, Johannes
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, evaluating these systems on all the world's languages is practically infea
Externí odkaz:
http://arxiv.org/abs/2407.05022
Neuromorphic computing mimics computational principles of the brain in $\textit{silico}$ and motivates research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity changes and offer supe
Externí odkaz:
http://arxiv.org/abs/2404.05858
An object handover between a robot and a human is a coordinated action which is prone to failure for reasons such as miscommunication, incorrect actions and unexpected object properties. Existing works on handover failure detection and prevention foc
Externí odkaz:
http://arxiv.org/abs/2402.18319
The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP. However, these improvements only apply to a small subset of the world's languages. Aiming to extend t
Externí odkaz:
http://arxiv.org/abs/2402.04222
While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological databases, including WALS and Grambank, suffer from inconsistencies prima
Externí odkaz:
http://arxiv.org/abs/2402.01513