7 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.CL2022
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?
Marina Sedinkina, Martin Schmitt, Hinrich Schütze
The practical success of much of NLP depends on the availability of training data. However, in real-world scenarios, training data is often scarce, not least because many applicati…
cs.CL2020★ 7 cited
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
Marina Sedinkina, Nikolas Breitkopf, Hinrich Schütze
In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (I) manual adaptation of the domain-general dictionary…