Learning Transductions and Alignments with RNN Seq2seq Models
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The paper studies the capabilities of Recurrent-Neural-Network sequence to sequence (RNN seq2seq) models in learning four string-to-string transduction tasks: identity, reversal, total reduplication, and input-specified reduplication. These transductions are traditionally well studied under finite state transducers and attributed with varying complexity. We find that RNN seq2seq models are only able to approximate a mapping that fits the training or in-distribution data. Attention helps significantly, but does not solve the out-of-distribution generalization limitation. Task complexity and RNN variants also play a role in the results. Our results are best understood in terms of the complexity hierarchy of formal languages as opposed to that of string transductions.
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