8 citations · 10 across the 4 of their papers we have counts for
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QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald +2
Question answering (QA) has recently shown impressive results for answering questions from customized domains. Yet, a common challenge is to adapt QA models to an unseen target dom…
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel
Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated…
Learning a Cost-Effective Annotation Policy for Question Answering
Bernhard Kratzwald, Stefan Feuerriegel, Huan Sun
State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems…
Practical Annotation Strategies for Question Answering Datasets
Bernhard Kratzwald, Xiang Yue, Huan Sun +1
Annotating datasets for question answering (QA) tasks is very costly, as it requires intensive manual labor and often domain-specific knowledge. Yet strategies for annotating QA da…
RankQA: Neural Question Answering with Answer Re-Ranking
Bernhard Kratzwald, Anna Eigenmann, Stefan Feuerriegel
The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently,…
Adaptive Document Retrieval for Deep Question Answering
Bernhard Kratzwald, Stefan Feuerriegel
State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural netwo…