Papers with metric based
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER (P18-2)
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| Challenge: | Existing approaches to improve NER performance add training data from one or more assisting languages to the primary language. |
| Approach: | They propose a metric based on symmetric KL divergence to filter out highly divergent training instances in the assisting language. |
| Outcome: | The proposed method improves NER performance in many languages, including those with limited training data. |
Not Every Metric is Equal: Cognitive Models for Predicting N400 and P600 Components During Reading Comprehension (2025.coling-main)
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| Challenge: | Several studies have focused on predicting the surprisal of a word and its reading time, but only recently, attention has been given to other components, such as P600. |
| Approach: | They propose to model reading times and ERP amplitudes using surprisal and entropy . they also propose a metric based on semantic similarity for N400 and P600 . |
| Outcome: | The proposed metric predicts reading times and ERP amplitudes in Mandarin Chinese. |
Transformer Based Multi-Source Domain Adaptation (2020.emnlp-main)
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| Challenge: | Existing approaches to improve machine learning performance are mixed experts and domain adversarial training. |
| Approach: | They investigate the problem of unsupervised multi-source domain adaptation . they combine predictions of multiple domain experts and combine them to induce a domain agnostic representation space . |
| Outcome: | The proposed methods improve models' performance while limiting learning time. |