Papers with finite-state models

2 papers
Neural Finite-State Transducers: Beyond Rational Relations (N19-1)

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Challenge: a finite state transducer defines joint and conditional probability distributions over strings . a weighted finite-state transducers can only model certain functions, known as the rational relations .
Approach: They propose a family of string transduction models defining joint and conditional probability distributions over pairs of strings.
Outcome: The proposed models are more powerful than previous finite-state models with neural features.
Recurrent Neural Language Models as Probabilistic Finite-state Automata (2023.emnlp-main)

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Challenge: Existing studies have focused on the expressive power of recurrent neural network LMs to recognize unweighted formal languages.
Approach: They propose to model a strict subset of probabilistic finite-state automata with RNNs . they show that an RNN requires left(N ||right) neurons to represent an LM .
Outcome: The proposed language models can represent a strict subset of probabilistic distributions expressed by finite-state models.

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