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Serial Recall Effects in Neural Language Modeling

Hassan Hajipoor, Hadi Amiri, Maseud Rahgozar, Farhad Oroumchian


Abstract
Serial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: word length and frequency, primacy and recency, semantic confusion, repetition, and transposition effects. In this research, we investigate neural language models in the context of these serial recall effects. Our work provides a framework to better understand and analyze neural language models and opens a new window to develop accurate language models.
Anthology ID:
N19-1073
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
688–694
Language:
URL:
https://aclanthology.org/N19-1073
DOI:
10.18653/v1/N19-1073
Bibkey:
Cite (ACL):
Hassan Hajipoor, Hadi Amiri, Maseud Rahgozar, and Farhad Oroumchian. 2019. Serial Recall Effects in Neural Language Modeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 688–694, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Serial Recall Effects in Neural Language Modeling (Hajipoor et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1073.pdf
Video:
 https://aclanthology.org/N19-1073.mp4