Papers with statistical and neural methods
Joint models for NLP (D18-3)
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| Challenge: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
| Approach: | This tutorial reviews main approaches to joint modeling for both statistical and neural methods. |
| Outcome: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)
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| Challenge: | Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics. |
| Approach: | They propose to standardize experimental settings for better comparability between methods . they compare existing methods with those proposed by decade-old models . |
| Outcome: | The proposed methods perform better than decade-old models on English and Japanese, respectively, compared with decade- old models. |
Can we teach language models to gloss endangered languages? (2024.findings-emnlp)
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| Challenge: | Prior research has explored statistical and neural methods for automatically producing IGT. |
| Approach: | They propose to use in-context learning to generate interlinear glossed text . they propose to employ supervised learning to select examples to provide in-text . |
| Outcome: | The proposed methods beat standard transformer baselines, despite requiring no training at all. |