LSTMs Exploit Linguistic Attributes of Data

@inproceedings{Liu2018LSTMsEL,
  title={LSTMs Exploit Linguistic Attributes of Data},
  author={Nelson F. Liu and Omer Levy and Roy Schwartz and Chenhao Tan and Noah A. Smith},
  booktitle={Rep4NLP@ACL},
  year={2018}
}
While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to learn a nonlinguistic task: recalling elements from its input. We find that models trained on natural language data are able to recall tokens from much longer sequences than models trained on non-language sequential data. Furthermore, we show that the LSTM… CONTINUE READING
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