Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Challenges and Thrills of Legal Arguments   State-of-the-art attention based models, mostly centered around the
transformer architecture, solve the problem of sequence-to-sequence translation
using the so-called scaled dot-product attention. While this technique is
highly effective for estimating inter-token attention, it does not answer the
question of inter-sequence attention when we deal with conversation-like
scenarios. We propose an extension, HumBERT, that attempts to perform
continuous contextual argument generation using locally trained transformers.

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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio