How has machine learning been used in connection with formal language theory?
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Machine learning has been used in connection with formal language theory to explore the relationship between neural network architectures and formal languages . Researchers have investigated the internal representations used by recurrent neural networks (RNNs) while learning to recognize regular formal languages. They have found that there is a decoding function that maps states of the RNN to states of the minimal deterministic finite automaton (MDFA) for the language . This decoding function maps RNN states to an abstraction obtained by clustering small sets of MDFA states into "superstates" . The results suggest a strong structural relationship between the internal representations used by RNNs and finite automata, providing insights into the ability of RNNs to recognize formal grammatical structure .
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Open access•Proceedings Article 01 Jan 2019 | Machine learning has been used to train recurrent neural networks (RNNs) to recognize regular formal languages, and to investigate the relationship between the internal representations of RNNs and finite automata. |
Open access•Posted Content | The paper investigates the internal representations used by recurrent neural networks (RNNs) while learning to recognize a regular formal language, suggesting a strong structural relationship between RNNs and finite automata. |
Open access•Posted Content | The paper discusses the connection between formal language theory and modern NLP, but it does not specifically mention how machine learning has been used in connection with formal language theory. |
27 May 2021 | Machine learning has been used to represent, recognize, and generate words from specific languages by learning from positive and negative samples of words in the language. |
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