Towards Quantum Language Models

Ivano Basile, Fabio Tamburini


Abstract
This paper presents a new approach for building Language Models using the Quantum Probability Theory, a Quantum Language Model (QLM). It mainly shows that relying on this probability calculus it is possible to build stochastic models able to benefit from quantum correlations due to interference and entanglement. We extensively tested our approach showing its superior performances, both in terms of model perplexity and inserting it into an automatic speech recognition evaluation setting, when compared with state-of-the-art language modelling techniques.
Anthology ID:
D17-1196
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1840–1849
Language:
URL:
https://aclanthology.org/D17-1196
DOI:
10.18653/v1/D17-1196
Bibkey:
Cite (ACL):
Ivano Basile and Fabio Tamburini. 2017. Towards Quantum Language Models. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1840–1849, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Towards Quantum Language Models (Basile & Tamburini, EMNLP 2017)
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PDF:
https://aclanthology.org/D17-1196.pdf
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 D17-1196.Attachment.zip