Abstract
We describe a data-driven approach for automatically explaining new, non-standard English expressions in a given sentence, building on a large dataset that includes 15 years of crowdsourced examples from UrbanDictionary.com. Unlike prior studies that focus on matching keywords from a slang dictionary, we investigate the possibility of learning a neural sequence-to-sequence model that generates explanations of unseen non-standard English expressions given context. We propose a dual encoder approach—a word-level encoder learns the representation of context, and a second character-level encoder to learn the hidden representation of the target non-standard expression. Our model can produce reasonable definitions of new non-standard English expressions given their context with certain confidence.- Anthology ID:
- I17-2070
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 413–417
- Language:
- URL:
- https://aclanthology.org/I17-2070
- DOI:
- Cite (ACL):
- Ke Ni and William Yang Wang. 2017. Learning to Explain Non-Standard English Words and Phrases. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 413–417, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Learning to Explain Non-Standard English Words and Phrases (Ni & Wang, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/I17-2070.pdf