Paper

Probing RNN Encoder-Decoder Generalization of Subregular Functions using Reduplication

Authors: Max Nelson (University of Massachusetts Amherst) , Hossep Dolatian (Stony Brook University) , Jonathan Rawski (Stony Brook University) , Brandon Prickett (University of Massachusetts Amherst)

Abstract

This paper examines the generalization abilities of encoder-decoder networks on a class of subregular functions characteristic of natural language reduplication. We find that, for the simulations we run, attention is a necessary and sufficient mechanism for learning generalizable reduplication. We examine attention alignment to connect RNN computation to a class of 2-way transducers.

Keywords: encoder-decoder, neural network, reduplication, seq2seq, finite-state tranducer, complexity, attention

How to Cite: Nelson, M. , Dolatian, H. , Rawski, J. & Prickett, B. (2020) “Probing RNN Encoder-Decoder Generalization of Subregular Functions using Reduplication”, Society for Computation in Linguistics. 3(1). doi: https://doi.org/10.7275/xd0r-pg04