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Simple Search
Hits 1 – 17 of 17
1
MasakhaNER: Named entity recognition for African languages
Adelani, David,
;
Abbott, Jade
;
Neubig, Graham
...
In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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2
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
Pratapa, Adithya
;
Anastasopoulos, Antonios
;
Rijhwani, Shruti
. - : arXiv, 2021
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3
Explorations in Transfer Learning for OCR Post-Correction ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Neubig, Graham
;
Rijhwani, Shruti
. - : Underline Science Inc., 2021
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4
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Chaudhary, Aditi
. - : Underline Science Inc., 2021
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5
Lexically Aware Semi-Supervised Learning for OCR Post-Correction ...
Rijhwani, Shruti
;
Rosenblum, Daisy
;
Anastasopoulos, Antonios
. - : arXiv, 2021
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6
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Anastasopoulos, Antonios
;
Neubig, Graham
. - : Underline Science Inc., 2021
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7
Dependency Induction Through the Lens of Visual Perception ...
Su, Ruisi
;
Rijhwani, Shruti
;
Zhu, Hao
. - : arXiv, 2021
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8
Dependency Induction Through the Lens of Visual Perception ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Hao
;
Bisk, Yonatan
. - : Underline Science Inc., 2021
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9
AlloVera: a multilingual allophone database
Mortensen, David,
;
Li, Xinjian
;
Littell, Patrick
...
In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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10
AlloVera: A Multilingual Allophone Database ...
Mortensen, David R.
;
Li, Xinjian
;
Littell, Patrick
. - : arXiv, 2020
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11
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ...
Neubig, Graham
;
Rijhwani, Shruti
;
Palmer, Alexis
. - : arXiv, 2020
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12
Temporally-Informed Analysis of Named Entity Recognition ...
Rijhwani, Shruti
;
Preoțiuc-Pietro, Daniel
. - : Zenodo, 2020
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13
Temporally-Informed Analysis of Named Entity Recognition ...
Rijhwani, Shruti
;
Preoțiuc-Pietro, Daniel
. - : Zenodo, 2020
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14
AlloVera: a multilingual allophone database
Mortensen, David,
;
Li, Xinjian
;
Littell, Patrick
...
In: LREC 2020: 12th Language Resources and Evaluation Conference ; https://halshs.archives-ouvertes.fr/halshs-02527046 ; LREC 2020: 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/ (2020)
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15
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
Zhou, Shuyan
;
Rijhwani, Shruti
;
Wieting, John
...
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020)
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16
Towards Zero-resource Cross-lingual Entity Linking ...
Zhou, Shuyan
;
Rijhwani, Shruti
;
Neubig, Graham
. - : arXiv, 2019
Abstract:
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, much previous work on XEL has been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are unavailable in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. Next, we propose three improvements to both entity candidate generation and disambiguation that make better use of the limited data we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-23% in end-to-end linking accuracy. ... : Accepted by EMNLP DeepLo workshop 2019 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/1909.13180
https://dx.doi.org/10.48550/arxiv.1909.13180
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17
Zero-shot Neural Transfer for Cross-lingual Entity Linking ...
Rijhwani, Shruti
;
Xie, Jiateng
;
Neubig, Graham
. - : arXiv, 2018
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