8,799 research outputs found
Large Margin Neural Language Model
We propose a large margin criterion for training neural language models.
Conventionally, neural language models are trained by minimizing perplexity
(PPL) on grammatical sentences. However, we demonstrate that PPL may not be the
best metric to optimize in some tasks, and further propose a large margin
formulation. The proposed method aims to enlarge the margin between the "good"
and "bad" sentences in a task-specific sense. It is trained end-to-end and can
be widely applied to tasks that involve re-scoring of generated text. Compared
with minimum-PPL training, our method gains up to 1.1 WER reduction for speech
recognition and 1.0 BLEU increase for machine translation.Comment: 9 pages. Accepted as a long paper in EMNLP201
Dirac series of
Using the sharpened Helgason-Johnson bound, this paper classifies all the
irreducible unitary representations with non-zero Dirac cohomology of
. As an application, we find that the cancellation between the even
part and the odd part of the Dirac cohomology continues to happen for certain
unitary representations of . Assuming the infinitesimal character
being integral, we further improve the Helgason-Johnson bound for .
This should help people to understand (part of) the unitary dual of this group.Comment: 25 pages. arXiv admin note: text overlap with arXiv:2204.0790
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