Search Results for author: Stefan Engl

Found 2 papers, 2 papers with code

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

1 code implementation • 31 May 2021 • David Peer, Sebastian Stabinger, Stefan Engl, Antonio Rodriguez-Sanchez

Knowledge distillation maintains high performance and reaches high compression rates, nevertheless, the size of the student model is fixed after pre-training and can not be changed individually for a given downstream task and use-case to reach a desired performance/speedup ratio.

Knowledge Distillation Unsupervised Pre-training

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

3 code implementations • LREC 2020 • Alexander Rietzler, Sebastian Stabinger, Paul Opitz, Stefan Engl

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 reviews can be leveraged to create value for businesses and customers.

Ranked #7 on Aspect-Based Sentiment Analysis (ABSA) on SemEval-2014 Task-4 (using extra training data)

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +5

868

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