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arXiv · 1810.12411

Counting in Language with RNNs

Heng xin Fun·Sergiy V Bokhnyak·Francesco Saverio Zuppichini
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Abstract

In this paper we examine a possible reason for the LSTM outperforming the GRU on language modeling and more specifically machine translation. We hypothesize that this has to do with counting. This is a consistent theme across the literature of long term dependence, counting, and language modeling for RNNs. Using the simplified forms of language -- Context-Free and Context-Sensitive Languages -- we show how exactly the LSTM performs its counting based on their cell states during inference and why the GRU cannot perform as well.

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Heng xin Fun, Sergiy V Bokhnyak, Francesco Saverio Zuppichini. 2018-10-29. Counting in Language with RNNs. https://arxiv.org/abs/1810.12411

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