Resource-rational Lossy Context Surprisal (LCS) implementation and related analysis for McCurdy and Hahn (2024).
This directory contains Python code to implement subword-tokenization-compatible LCS.
This directory contains R code to reproduce the English relative clause analysis reported in the CoNLL paper.
If you use this code, please cite the following references:
@inproceedings{mccurdy_lossy_2024,
address = {Miami, FL, USA},
title = {Lossy {Context} {Surprisal} {Predicts} {Task}-{Dependent} {Patterns} in {Relative} {Clause} {Processing}},
url = {https://aclanthology.org/2024.conll-1.4},
booktitle = {Proceedings of the 28th {Conference} on {Computational} {Natural} {Language} {Learning}},
publisher = {Association for Computational Linguistics},
author = {McCurdy, Kate and Hahn, Michael},
editor = {Barak, Libby and Alikhani, Malihe},
month = nov,
year = {2024},
pages = {36--45}
}
@article{hahn_resource-rational_2022,
title = {A resource-rational model of human processing of recursive linguistic structure},
volume = {119},
url = {https://www.pnas.org/doi/10.1073/pnas.2122602119},
doi = {10.1073/pnas.2122602119},
number = {43},
urldate = {2023-04-23},
journal = {Proceedings of the National Academy of Sciences},
author = {Hahn, Michael and Futrell, Richard and Levy, Roger and Gibson, Edward},
month = oct,
year = {2022},
}