Papers with machine learning problems
Demo Application for the AutoGOAL Framework (2020.coling-demos)
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Suilan Estevez-Velarde, Alejandro Piad-Morffis, Yoan Gutiérrez, Andres Montoyo, Rafael Muñoz-Guillena, Yudivián Almeida Cruz
| Challenge: | AutoGOAL is a framework for automatically finding the best way to solve a given computational task. |
| Approach: | They present a web demo that showcases the main characteristics of the AutoGOAL framework in Python and a graph-based representation for machine learning pipelines. |
| Outcome: | The proposed framework can be applied to Natural Language Processing and structured classification problems. |
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing (2020.coling-main)
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| Challenge: | Existing AutoML systems use heterogeneous techniques to build pipelines that combine techniques and algorithms from different frameworks. |
| Approach: | They propose a system for automatic machine learning that uses heterogeneous techniques. |
| Outcome: | The proposed system is evaluated in diverse machine learning problems and compared with other alternatives. |
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements (2022.lrec-1)
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| Challenge: | a paper presents text mining approaches on German-speaking job advertisements . transfer learning and domain adaptation are used to build text mining applications . |
| Approach: | They propose text mining approaches on German-speaking job advertisements . they use transfer learning and domain adaptation to build language models adapted to job ads . |
| Outcome: | The proposed approaches outperform general-domain language models pre-trained on ten times more data. |