@inproceedings{zhang-nie-2020-inducing,
    title = "Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition",
    author = "Zhang, Yuhui  and
      Nie, Allen",
    editor = "Rijhwani, Shruti  and
      Liu, Jiangming  and
      Wang, Yizhong  and
      Dror, Rotem",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-srw.40/",
    doi = "10.18653/v1/2020.acl-srw.40",
    pages = "299--305",
    abstract = "The principle of compositionality has deep roots in linguistics: the meaning of an expression is determined by its structure and the meanings of its constituents. However, modern neural network models such as long short-term memory network process expressions in a linear fashion and do not seem to incorporate more complex compositional patterns. In this work, we show that we can explicitly induce grammar by tracing the computational process of a long short-term memory network. We show: (i) the multiplicative nature of long short-term memory network allows complex interaction beyond sequential linear combination; (ii) we can generate compositional trees from the network without external linguistic knowledge; (iii) we evaluate the syntactic difference between the generated trees, randomly generated trees and gold reference trees produced by constituency parsers; (iv) we evaluate whether the generated trees contain the rich semantic information."
}