Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System
| Autor: | Lifeng Jin, Amad Hussein, Douglas R. Danforth, David L. King, Michael White |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Frequently asked questions 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Virtual patient 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | BEA@NAACL-HLT |
| DOI: | 10.18653/v1/w18-0502 |
| Popis: | When interpreting questions in a virtual patient dialogue system one must inevitably tackle the challenge of a long tail of relatively infrequently asked questions. To make progress on this challenge, we investigate the use of paraphrasing for data augmentation and neural memory-based classification, finding that the two methods work best in combination. In particular, we find that the neural memory-based approach not only outperforms a straight CNN classifier on low frequency questions, but also takes better advantage of the augmented data created by paraphrasing, together yielding a nearly 10% absolute improvement in accuracy on the least frequently asked questions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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