Guenter Neumann — accepted AI-conference papers

2022

MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction

Saadullah Amin, Pasquale Minervini, David Chang, Pontus Stenetorp, Guenter Neumann

COLING 2022main

Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used to tackle the scarcity of annotated data by automatically pairing knowledge graph relationships with raw texts. Such a p…

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2021

AutoEQA: Auto-Encoding Questions for Extractive Question Answering

Stalin Varanasi, Saadullah Amin, Guenter Neumann

EMNLP 2021finding

There has been a significant progress in the field of Extractive Question Answering (EQA) in the recent years. However, most of them are reliant on annotations of answer-spans in the corresponding passages. In this work, we address the problem of EQA when no annotations are present for the answer sp…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗