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Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 69-81 (2021) (2021)
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Typology-aware neural dependency parsing : challenges and directions
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Abstract:
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 ; Cataloged from student-submitted PDF version of thesis. ; Includes bibliographical references (pages 39-41). ; This thesis explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of the investigation put forth in this thesis is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing. Code for the work in this thesis is available at https://github.com/ajfisch/TypologyParser. ; by Adam Fisch. ; S.M. ; S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Keyword:
Electrical Engineering and Computer Science
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URL: https://hdl.handle.net/1721.1/128400
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Transfer learning for low-resource natural language analysis
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Hierarchical low-rank tensors for multilingual transfer parsing
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In: http://aclweb.org/anthology/D/D15/D15-1213.pdf (2015)
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Context-dependent type-level models for unsupervised morpho-syntactic induction
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Linguistically Motivated Models for Lightly-Supervised Dependency Parsing
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In: http://people.csail.mit.edu/tahira/main.pdf (2014)
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Low-rank tensors for scoring dependency structures
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In: http://people.csail.mit.edu/tommi/papers/Lei-ACL14.pdf (2014)
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The MIT Faculty has made this article openly available. Please share how this access benefits you
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In: http://dspace.mit.edu/openaccess-disseminate/1721.1/59314/ (2014)
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Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches
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In: http://dspace.mit.edu/openaccess-disseminate/1721.1/62804/ (2014)
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Linguistically motivated models for lightly-supervised dependency parsing
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Morphological segmentation : an unsupervised method and application to Keyword Spotting ; Unsupervised method and application to KWS
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Learning to map into a universal pos tagset
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In: http://people.csail.mit.edu/yuanzh/papers/emnlp2012.pdf (2012)
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Grounding Linguistic Analysis in Control Applications
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In: http://people.csail.mit.edu/branavan/papers/branavan-thesis.pdf (2012)
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In-domain relation discovery with meta-constraints via posterior regularization
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In: http://people.csail.mit.edu/regina/my_papers/sem_acl2011.pdf (2011)
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Learning to win by reading manuals in a monte-carlo framework
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In: http://www.aclweb.org/anthology/P11-1028/ (2011)
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Non-linear monte-carlo search in civilization II
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In: http://people.csail.mit.edu/branavan/papers/ijcai2011.pdf (2011)
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