Abstract
Decoding of phrase-based translation models in the general case is known to be NP-complete, by a reduction from the traveling salesman problem (Knight, 1999). In practice, phrase-based systems often impose a hard distortion limit that limits the movement of phrases during translation. However, the impact on complexity after imposing such a constraint is not well studied. In this paper, we describe a dynamic programming algorithm for phrase-based decoding with a fixed distortion limit. The runtime of the algorithm is O(nd!lhd+1) where n is the sentence length, d is the distortion limit, l is a bound on the number of phrases starting at any position in the sentence, and h is related to the maximum number of target language translations for any source word. The algorithm makes use of a novel representation that gives a new perspective on decoding of phrase-based models.- Anthology ID:
- Q17-1005
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 5
- Month:
- Year:
- 2017
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 59–71
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/Q17-1005/
- DOI:
- 10.1162/tacl_a_00046
- Cite (ACL):
- Yin-Wen Chang and Michael Collins. 2017. A Polynomial-Time Dynamic Programming Algorithm for Phrase-Based Decoding with a Fixed Distortion Limit. Transactions of the Association for Computational Linguistics, 5:59–71.
- Cite (Informal):
- A Polynomial-Time Dynamic Programming Algorithm for Phrase-Based Decoding with a Fixed Distortion Limit (Chang & Collins, TACL 2017)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/Q17-1005.pdf