Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new State-of-the-Art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing, though. With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach to the unit segmentation task. We also compare sentence-level contextualized word embeddings to pre-generated ones. Our findings suggest that for this task the additional attention layer does not improve upon a less complex approach. In most cases, the contextualized embeddings do also not show an improvement on the baseline score.
@article{arxiv.1906.10068,
title = {Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation},
author = {Maximilian Spliethöver and Jonas Klaff and Hendrik Heuer},
journal= {arXiv preprint arXiv:1906.10068},
year = {2019}
}
Comments
Accepted to the 6th Workshop on Argument Mining 2019