Papers with machine learning perspective
A Deep Generative Approach to Native Language Identification (2020.coling-main)
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| Challenge: | Native language identification (NLI) is a multi-class classification task involving multiple features that capture the systematic fingerprints of the first language in the second language writing. |
| Approach: | They propose a deep generative language modelling approach to NLI that fine-tunes a GPT-2 model separately on texts written by the authors with the same L1 and assigns n-grams to an unseen text. |
| Outcome: | The proposed method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets. |
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (P18-1)
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| Challenge: | a recent study shows that counterfactual learning from human bandit feedback can improve neural semantic parsers . cost and difficulty of manually preparing large amounts of parses is a bottleneck for supervised learning . |
| Approach: | They propose to use human bandit feedback to apply counterfactual learning to neural parsing . they devise an easy-to-use interface to collect human feedback on semantic parses . |
| Outcome: | The proposed framework improves semantic parsers by reducing the cost of manual parsing . the proposed framework is based on human bandit feedback collected by the user . |