@inproceedings{nematzadeh-etal-2019-language,
title = "Language Learning and Processing in People and Machines",
author = "Nematzadeh, Aida and
Futrell, Richard and
Levy, Roger",
editor = "Sarkar, Anoop and
Strube, Michael",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-5005",
doi = "10.18653/v1/N19-5005",
pages = "19--21",
abstract = "The goal of this tutorial is to bring the fields of computational linguistics and computational cognitive science closer: we will introduce different stages of language acquisition and their parallel problems in NLP. As an example, one of the early challenges children face is mapping the meaning of word labels (such as {``}cat{''}) to their referents (the furry animal in the living room). Word learning is similar to the word alignment problem in machine translation. We explain the current computational models of language acquisition, their limitations, and how the insights from these models can be incorporated into NLP applications. Moreover, we discuss how we can take advantage of the cognitive science of language in computational linguistics: for example, by designing cognitively-motivated evaluations task or buildings language-learning inductive biases into our models.",
}
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%0 Conference Proceedings
%T Language Learning and Processing in People and Machines
%A Nematzadeh, Aida
%A Futrell, Richard
%A Levy, Roger
%Y Sarkar, Anoop
%Y Strube, Michael
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F nematzadeh-etal-2019-language
%X The goal of this tutorial is to bring the fields of computational linguistics and computational cognitive science closer: we will introduce different stages of language acquisition and their parallel problems in NLP. As an example, one of the early challenges children face is mapping the meaning of word labels (such as “cat”) to their referents (the furry animal in the living room). Word learning is similar to the word alignment problem in machine translation. We explain the current computational models of language acquisition, their limitations, and how the insights from these models can be incorporated into NLP applications. Moreover, we discuss how we can take advantage of the cognitive science of language in computational linguistics: for example, by designing cognitively-motivated evaluations task or buildings language-learning inductive biases into our models.
%R 10.18653/v1/N19-5005
%U https://aclanthology.org/N19-5005
%U https://doi.org/10.18653/v1/N19-5005
%P 19-21
Markdown (Informal)
[Language Learning and Processing in People and Machines](https://aclanthology.org/N19-5005) (Nematzadeh et al., NAACL 2019)
ACL
- Aida Nematzadeh, Richard Futrell, and Roger Levy. 2019. Language Learning and Processing in People and Machines. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pages 19–21, Minneapolis, Minnesota. Association for Computational Linguistics.