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Author
Date
2021Type
- Doctoral Thesis
ETH Bibliography
yes
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Abstract
Question Answering (QA) is the task of automatically deriving answers from natural language questions. It fundamentally redefines how humans interact with information systems by replacing keyword search or technical queries with interactions in natural language. The recent advancement of deep learning and neural networks has lead to significant performance gains in QA. Yet, the performance of QA systems often drops significantly when they are subject to a domain shift, i.e., questions used to train the system differ from user questions after deployment. This represents a severe problem in many practical setups, where certain guarantees on the performance are required. Furthermore, it limits the application of QA to areas for which we have access to large amounts of training data; which is primarily open-domain questions in English language.
In this thesis, we present several solutions to tackle the problem of domain shift in QA.
On the one hand, we present two methods designed for a usage before the deployment of a QA system. This includes a method for transfer learning when having only access to a small labeled amount of training data, as well as a method for a cost-effective annotation of new datasets. On the other hand, we present a method for domain customization after deployment. Here, the QA system continuously learns to adapt to the new domain directly from user interactions and is capable to overcome an initially low performance over time. In addition, we present robust architectures for QA systems that help in addressing domain shift as a foundation of this thesis. Finally, we show how our methods can be extended to the different but related task of knowledge base completion. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000475835Publication status
publishedExternal links
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Publisher
ETH ZurichOrganisational unit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
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ETH Bibliography
yes
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