Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computation and Language

arXiv:1704.06393 (cs)
[Submitted on 21 Apr 2017]

Title:Neural System Combination for Machine Translation

Authors:Long Zhou, Wenpeng Hu, Jiajun Zhang, Chengqing Zong
View PDF
Abstract:Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT).
However, SMT is usually better than NMT in translation adequacy. It is therefore a promising direction to combine the advantages of both NMT and SMT.
In this paper, we propose a neural system combination framework leveraging multi-source NMT, which takes as input the outputs of NMT and SMT systems and produces the final translation.
Extensive experiments on the Chinese-to-English translation task show that our model archives significant improvement by 5.3 BLEU points over the best single system output and 3.4 BLEU points over the state-of-the-art traditional system combination methods.
Comments: Accepted as a short paper by ACL-2017
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:1704.06393 [cs.CL]
  (or arXiv:1704.06393v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.1704.06393

Submission history

From: Jiajun Zhang [view email]
[v1] Fri, 21 Apr 2017 04:36:55 UTC (211 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2017-04
Change to browse by:
cs

References & Citations

DBLP - CS Bibliography

listing | bibtex
Long Zhou
Wenpeng Hu
Jiajun Zhang
Chengqing Zong
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)