Papers with bootstrapping method
Joint Bootstrapping Machines for High Confidence Relation Extraction (N18-1)
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| Challenge: | Existing semi-supervised bootstrapping methods for relationship extraction lack labeled data. |
| Approach: | They propose a semi-supervised bootstrapping method that protects against semantic drift . they expand entities and templates in parallel and in mutually constraining fashion in each iteration . |
| Outcome: | Experimental results show that BREX improves on state-of-the-art methods for four relationships. |
Learning to Bootstrap for Entity Set Expansion (D19-1)
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| Challenge: | Existing bootstrapping methods for Entity Set Expansion suffer from two problems: 1) delayed feedback and sparse supervision. |
| Approach: | They propose a method that estimates delayed feedback and adaptively scores entities given sparse supervision signals. |
| Outcome: | The proposed method can estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. |
Minimal Supervision for Morphological Inflection (2021.emnlp-main)
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| Challenge: | Neural models for morphological reinflection tasks have proved to be extremely accurate given ample labeled data, yet labele d data may be slow and costly to obtain. |
| Approach: | They exploit orthographic and semantic regularities in morphological systems to exploit the orthographic regularities on their own to achieve respectable accuracy. |
| Outcome: | The bootstrapping method outperforms hallucination-based methods for morphological reinflection tasks. |
Establishing Annotation Quality in Multi-label Annotations (2022.coling-1)
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| Challenge: | Multi-label annotations allow multiple interpretations of a single item, but they also affect the chance that two coders agree with each other. |
| Approach: | They propose a bootstrapped method to obtain chance agreement for each measure and a method to get an adjusted agreement coefficient that is more interpretable. |
| Outcome: | The proposed method allows for an adjusted agreement coefficient that is more interpretable on simulated datasets. |
TwittIrish: A Universal Dependencies Treebank of Tweets in Modern Irish (2022.acl-long)
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| Challenge: | Modern Irish is a minority language lacking computational resources for accurate automatic syntactic parsing of user-generated content. |
| Approach: | They propose to use a treebank to facilitate natural language parsing of user-generated content in Irish. |
| Outcome: | The proposed treebank enables natural language processing of user-generated content in Irish. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
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| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |