Papers by Zaid Sheikh
Reducing Confusion in Active Learning for Part-Of-Speech Tagging (2021.tacl-1)
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| Challenge: | Existing algorithms for annotating parts of speech are not optimal for all languages. |
| Approach: | They propose to use a data selection algorithm to select useful training samples to minimize annotation cost. |
| Outcome: | The proposed strategy outperforms existing strategies on six typologically diverse languages. |
Automatic Extraction of Rules Governing Morphological Agreement (2020.emnlp-main)
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Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig
| Challenge: | Creating a descriptive grammar is an indispensable step for language documentation but it is tedious and time-consuming. |
| Approach: | They propose a framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. |
| Outcome: | The proposed framework extracts a grammatical specification that is nearly equivalent to those created with large amounts of gold-standard annotated data. |
Parser combinators for Tigrinya and Oromo morphology (L18-1)
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Patrick Littell, Tom McCoy, Na-Rae Han, Shruti Rijhwani, Zaid Sheikh, David Mortensen, Teruko Mitamura, Lori Levin
| Challenge: | morphological parsers for two Afroasiatic languages are developed using a parser-combinator paradigm . the paradigm allows rapid development and ease of integration with other systems, but at a cost of non-optimal theoretical efficiency. |
| Approach: | They propose a rule-based morphological parser paradigm for Tigrinya and Oromo languages . they use a parsers-combinator paradigm instead of a finite-state paradigm . |
| Outcome: | The proposed paradigm allows rapid development and ease of integration with other systems, but at cost of non-optimal theoretical efficiency. |
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |
MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking (2026.tacl-1)
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Sathyanarayanan Ramamoorthy, Vishwa Shah, Simran Khanuja, Zaid Sheikh, Shan Jie, Ann Chia, Shearman Chua, Graham Neubig
| Challenge: | Existing methods for multilingual entity linking are limited by textual contexts and limited resources. |
| Approach: | They propose a testbed system for multilingual multimodal entity linking using BBC news articles paired with corresponding images in five languages. |
| Outcome: | The proposed system improves accuracy for entities with ambiguous textual contexts and models with weak multilingual abilities. |