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cs.CL2021
Semantic Answer Similarity for Evaluating Question Answering Models
Julian Risch, Timo Möller, Julian Gutsch +1
The evaluation of question answering models compares ground-truth annotations with model predictions. However, as of today, this comparison is mostly lexical-based and therefore mi…
cs.CL2021
Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models
Bogdan Kostić, Julian Risch, Timo Möller
Open-domain extractive question answering works well on textual data by first retrieving candidate texts and then extracting the answer from those candidates. However, some questio…
cs.CL2021
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval
Timo Möller, Julian Risch, Malte Pietsch
A major challenge of research on non-English machine reading for question answering (QA) is the lack of annotated datasets. In this paper, we present GermanQuAD, a dataset of 13,72…