Dana Angluin
2018
Context-Free Transductions with Neural Stacks
Yiding Hao
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William Merrill
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Dana Angluin
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Robert Frank
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Noah Amsel
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Andrew Benz
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Simon Mendelsohn
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, context-free language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.
2011
Effects of Meaning-Preserving Corrections on Language Learning
Dana Angluin
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Leonor Becerra-Bonache
Proceedings of the Fifteenth Conference on Computational Natural Language Learning
2009
Experiments Using OSTIA for a Language Production Task
Dana Angluin
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Leonor Becerra-Bonache
Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
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Co-authors
- Leonor Becerra-Bonache 2
- Yiding Hao 1
- William Merrill 1
- Robert Frank 1
- Noah Amsel 1
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Venues
- WS3