Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Is It Worth the Attention? A Comparative Evaluation of Attention Layers
  for Argument Unit Segmentation   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.

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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math