# Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

arXiv: https://arxiv.org/abs/1906.10068 · HTML: https://mlsanity.com/paper/1906.10068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)

## Abstract (arXiv)

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.