Zobrazeno 1 - 10
of 1 121
pro vyhledávání: '"Zeldes, A."'
Autor:
Liu, Yang Janet, Aoyama, Tatsuya, Scivetti, Wesley, Zhu, Yilun, Behzad, Shabnam, Levine, Lauren Elizabeth, Lin, Jessica, Tiwari, Devika, Zeldes, Amir
Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by no
Externí odkaz:
http://arxiv.org/abs/2411.00491
This study introduces a refined approach to Text-to-Speech (TTS) generation that significantly enhances sampling stability across languages, with a particular focus on Hebrew. By leveraging discrete semantic units with higher phonetic correlation obt
Externí odkaz:
http://arxiv.org/abs/2410.21502
Autor:
Levine, Lauren, Zeldes, Amir
Comparing bridging annotations across coreference resources is difficult, largely due to a lack of standardization across definitions and annotation schemas and narrow coverage of disparate text domains across resources. To alleviate domain coverage
Externí odkaz:
http://arxiv.org/abs/2410.01170
Lacuna Language Learning: Leveraging RNNs for Ranked Text Completion in Digitized Coptic Manuscripts
Ancient manuscripts are frequently damaged, containing gaps in the text known as lacunae. In this paper, we present a bidirectional RNN model for character prediction of Coptic characters in manuscript lacunae. Our best model performs with 72% accura
Externí odkaz:
http://arxiv.org/abs/2407.12247
Autor:
Turetzky, Arnon, Tal, Or, Segal-Feldman, Yael, Dissen, Yehoshua, Zeldes, Ella, Roth, Amit, Cohen, Eyal, Shrem, Yosi, Chernyak, Bronya R., Seleznova, Olga, Keshet, Joseph, Adi, Yossi
We present HebDB, a weakly supervised dataset for spoken language processing in the Hebrew language. HebDB offers roughly 2500 hours of natural and spontaneous speech recordings in the Hebrew language, consisting of a large variety of speakers and to
Externí odkaz:
http://arxiv.org/abs/2407.07566
Autor:
Weissweiler, Leonie, Böbel, Nina, Guiller, Kirian, Herrera, Santiago, Scivetti, Wesley, Lorenzi, Arthur, Melnik, Nurit, Bhatia, Archna, Schütze, Hinrich, Levin, Lori, Zeldes, Amir, Nivre, Joakim, Croft, William, Schneider, Nathan
The Universal Dependencies (UD) project has created an invaluable collection of treebanks with contributions in over 140 languages. However, the UD annotations do not tell the full story. Grammatical constructions that convey meaning through a partic
Externí odkaz:
http://arxiv.org/abs/2403.17748
Singleton mentions, i.e.~entities mentioned only once in a text, are important to how humans understand discourse from a theoretical perspective. However previous attempts to incorporate their detection in end-to-end neural coreference resolution for
Externí odkaz:
http://arxiv.org/abs/2403.17245
In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs
Externí odkaz:
http://arxiv.org/abs/2403.13560
Autor:
Lin, Jessica, Zeldes, Amir
As NLP models become increasingly capable of understanding documents in terms of coherent entities rather than strings, obtaining the most salient entities for each document is not only an important end task in itself but also vital for Information R
Externí odkaz:
http://arxiv.org/abs/2401.17974
Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as well as other entity information. This paper presents a coreference m
Externí odkaz:
http://arxiv.org/abs/2309.11582