Zobrazeno 1 - 10
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pro vyhledávání: '"Mayhew, Stephen"'
Publikováno v:
In Findings of the Association for Computational Linguistics (ACL 2024)
We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language learners. Using a novel framework, we evaluate the effectiveness of sev
Externí odkaz:
http://arxiv.org/abs/2406.03030
Autor:
Mayhew, Stephen, Blevins, Terra, Liu, Shuheng, Šuppa, Marek, Gonen, Hila, Imperial, Joseph Marvin, Karlsson, Börje F., Lin, Peiqin, Ljubešić, Nikola, Miranda, LJ, Plank, Barbara, Riabi, Arij, Pinter, Yuval
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standard
Externí odkaz:
http://arxiv.org/abs/2311.09122
Autor:
Mayhew, Stephen Richard
The wireless world of today is essential to the everyday life of millions of people. Wireless technology is evolving at a rapid pace that's speed outmatches what the previous testing can handle. This necessitates the need for smarter and faster testi
Externí odkaz:
http://hdl.handle.net/10919/110927
Autor:
Adelani, David Ifeoluwa, Abbott, Jade, Neubig, Graham, D'souza, Daniel, Kreutzer, Julia, Lignos, Constantine, Palen-Michel, Chester, Buzaaba, Happy, Rijhwani, Shruti, Ruder, Sebastian, Mayhew, Stephen, Azime, Israel Abebe, Muhammad, Shamsuddeen, Emezue, Chris Chinenye, Nakatumba-Nabende, Joyce, Ogayo, Perez, Aremu, Anuoluwapo, Gitau, Catherine, Mbaye, Derguene, Alabi, Jesujoba, Yimam, Seid Muhie, Gwadabe, Tajuddeen, Ezeani, Ignatius, Niyongabo, Rubungo Andre, Mukiibi, Jonathan, Otiende, Verrah, Orife, Iroro, David, Davis, Ngom, Samba, Adewumi, Tosin, Rayson, Paul, Adeyemi, Mofetoluwa, Muriuki, Gerald, Anebi, Emmanuel, Chukwuneke, Chiamaka, Odu, Nkiruka, Wairagala, Eric Peter, Oyerinde, Samuel, Siro, Clemencia, Bateesa, Tobius Saul, Oloyede, Temilola, Wambui, Yvonne, Akinode, Victor, Nabagereka, Deborah, Katusiime, Maurice, Awokoya, Ayodele, MBOUP, Mouhamadane, Gebreyohannes, Dibora, Tilaye, Henok, Nwaike, Kelechi, Wolde, Degaga, Faye, Abdoulaye, Sibanda, Blessing, Ahia, Orevaoghene, Dossou, Bonaventure F. P., Ogueji, Kelechi, DIOP, Thierno Ibrahima, Diallo, Abdoulaye, Akinfaderin, Adewale, Marengereke, Tendai, Osei, Salomey
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a v
Externí odkaz:
http://arxiv.org/abs/2103.11811
In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it. Cross-lingual methods have had notable success in addressing these concerns, but in certain c
Externí odkaz:
http://arxiv.org/abs/2006.09627
Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success has focused only on the top 104 languages in Wikipedia that it was trained on. In this paper, we propose a simp
Externí odkaz:
http://arxiv.org/abs/2004.13640
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data. In this work, we provide a comprehensive study of the contr
Externí odkaz:
http://arxiv.org/abs/1912.07840
Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data. In particular, capitalization is a strong signal for entities in many languages, and even sta
Externí odkaz:
http://arxiv.org/abs/1912.07095
Akademický článek
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Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We study the problem of Named Entity Recognition (NER) with partially annot
Externí odkaz:
http://arxiv.org/abs/1909.09270