Charmanteau: Character Embedding Models For Portmanteau Creation

Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy, Eric Nyberg


Abstract
Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.
Anthology ID:
D17-1315
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:
2917–2922
Language:
URL:
https://aclanthology.org/D17-1315
DOI:
10.18653/v1/D17-1315
Bibkey:
Cite (ACL):
Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy, and Eric Nyberg. 2017. Charmanteau: Character Embedding Models For Portmanteau Creation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2917–2922, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Charmanteau: Character Embedding Models For Portmanteau Creation (Gangal et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1315.pdf
Attachment:
 D17-1315.Attachment.zip
Video:
 https://vimeo.com/238231770
Code
 vgtomahawk/Charmanteau-CamReady