most citedAdapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification

134 citations · 169 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CL2021★ 35 cited

Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing

David Peer, Sebastian Stabinger, Stefan Engl +1

Fine-tuning transformer models after unsupervised pre-training reaches a very high performance on many different natural language processing tasks. Unfortunately, transformers suff…

cs.CL2019★ 134 cited

Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification

Alexander Rietzler, Sebastian Stabinger, Paul Opitz +1

Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from r…