Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Nima Pourdamghani"'
Autor:
Parminder Bhatia, Lan Liu, Kristjan Arumae, Nima Pourdamghani, Suyog Deshpande, Ben Snively, Mona Mona, Colby Wise, George Price, Shyam Ramaswamy, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang, Taha Kass-Hout
Publikováno v:
AI for Disease Surveillance and Pandemic Intelligence ISBN: 9783030930790
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90951d42f70ad67030272497384e4dbd
https://doi.org/10.1007/978-3-030-93080-6_11
https://doi.org/10.1007/978-3-030-93080-6_11
Autor:
Kevin Knight, Nima Pourdamghani
Publikováno v:
Machine Translation. 33:239-258
Sentence-level parallel data is essential for training machine translation systems. However, existing parallel data is extremely limited for thousands of languages. In order to increase the available parallel data for a low-resource language we borro
Autor:
Yogarshi Vyas, Kathleen R. McKeown, Yaser Al-Onaizan, Rishita Anubhai, Shuai Wang, Parminder Bhatia, Nima Pourdamghani, Jie Ma, Miguel Ballesteros
Publikováno v:
EMNLP (1)
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Befo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bb3ee7958d5a1ec8f49e85382457477
http://arxiv.org/abs/2004.04295
http://arxiv.org/abs/2004.04295
Autor:
Xing Shi, Qiang Li, Ulf Hermjakob, Daniel Marcu, Nima Pourdamghani, Kevin Knight, Xiaoman Pan, Ying Lin, Tomer Levinboim, Jonathan May, Boliang Zhang, Di Lu, Sebastian J. Mielke, Michael Pust, David Chiang, Heng Ji, Kenton Murray
Publikováno v:
Machine Translation. 32:59-89
We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHL
Publikováno v:
ACL (1)
Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: genera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e6cba1425ab25b0426ac315e2155da5
http://arxiv.org/abs/1906.05683
http://arxiv.org/abs/1906.05683
Autor:
Boliang Zhang, Colin Vaz, Heng Ji, Lukas Burget, Di Lu, Ying Lin, Martin Karafiat, Shrikanth S. Narayanan, Kevin Knight, Mark Hasegawa-Johnson, Jonathan May, Michael Pust, Ondřej Glembek, Pavlos Papadopoulos, Murali Karthick Baskar, Nima Pourdamghani, Ruchir Travadi, Nikolaos Malandrakis, Xiaoman Pan, Ulf Hermjakob
Publikováno v:
INTERSPEECH
Autor:
Kevin Knight, Nima Pourdamghani
Publikováno v:
EMNLP
We present a method for translating texts between close language pairs. The method does not require parallel data, and it does not require the languages to be written in the same script. We show results for six language pairs: Afrikaans/Dutch, Bosnia
Publikováno v:
ACL (1)
Publikováno v:
INLG
Publikováno v:
Pattern Recognition Letters. 33:1529-1535
In this letter, we introduce a semi-supervised manifold regularization framework for human pose estimation. We utilize the unlabeled data to compensate for the complexities in the input space and model the underlying manifold by a nearest neighbor gr