163 citations · 397 across the 15 of their papers we have counts for
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cs.CL2021★ 11 cited
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference
William Held, Dan Iter, Dan Jurafsky
Performing event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the full pairwise compariso…
cs.CL2021★ 6 cited
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation
Dan Iter, David Grangier
Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning. Data selection improves target domain gener…
cs.CL2021
The Trade-offs of Domain Adaptation for Neural Language Models
David Grangier, Dan Iter
This work connects language model adaptation with concepts of machine learning theory. We consider a training setup with a large out-of-domain set and a small in-domain set. We der…