@inproceedings{stein-etal-2021-shapelurn,
title = "{SHAPELURN}: An Interactive Language Learning Game with Logical Inference",
author = "Stein, Katharina and
Harter, Leonie and
Geiger, Luisa",
editor = {Brantley, Kiant{\'e} and
Dan, Soham and
Gurevych, Iryna and
Lee, Ji-Ung and
Radlinski, Filip and
Sch{\"u}tze, Hinrich and
Simpson, Edwin and
Yu, Lili},
booktitle = "Proceedings of the First Workshop on Interactive Learning for Natural Language Processing",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.internlp-1.3/",
doi = "10.18653/v1/2021.internlp-1.3",
pages = "16--24",
abstract = "We investigate if a model can learn natural language with minimal linguistic input through interaction. Addressing this question, we design and implement an interactive language learning game that learns logical semantic representations compositionally. Our game allows us to explore the benefits of logical inference for natural language learning. Evaluation shows that the model can accurately narrow down potential logical representations for words over the course of the game, suggesting that our model is able to learn lexical mappings from scratch successfully."
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%0 Conference Proceedings
%T SHAPELURN: An Interactive Language Learning Game with Logical Inference
%A Stein, Katharina
%A Harter, Leonie
%A Geiger, Luisa
%Y Brantley, Kianté
%Y Dan, Soham
%Y Gurevych, Iryna
%Y Lee, Ji-Ung
%Y Radlinski, Filip
%Y Schütze, Hinrich
%Y Simpson, Edwin
%Y Yu, Lili
%S Proceedings of the First Workshop on Interactive Learning for Natural Language Processing
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F stein-etal-2021-shapelurn
%X We investigate if a model can learn natural language with minimal linguistic input through interaction. Addressing this question, we design and implement an interactive language learning game that learns logical semantic representations compositionally. Our game allows us to explore the benefits of logical inference for natural language learning. Evaluation shows that the model can accurately narrow down potential logical representations for words over the course of the game, suggesting that our model is able to learn lexical mappings from scratch successfully.
%R 10.18653/v1/2021.internlp-1.3
%U https://aclanthology.org/2021.internlp-1.3/
%U https://doi.org/10.18653/v1/2021.internlp-1.3
%P 16-24
Markdown (Informal)
[SHAPELURN: An Interactive Language Learning Game with Logical Inference](https://aclanthology.org/2021.internlp-1.3/) (Stein et al., InterNLP 2021)
ACL