Alibaba Submission to the WMT18 Parallel Corpus Filtering Task
By Jun Lu, Xiaoyu Lv, Yangbin Shi, Boxing Chen. 2018
31 citations recorded by the source corpus.
This paper describes the Alibaba Machine Translation Group submissions to the WMT 2018 Shared Task on Parallel Corpus Filtering. While evaluating the quality of the parallel corpus, the three characteristics of the corpus are investigated, i.e. 1) the bilingual/translation quality, 2) the monolingual quality and 3) the corpus diversity. Both rule-based and model-based methods are adapted to score the parallel sentence pairs. The final parallel corpus filtering system is reliable, easy to build and adapt to other language pairs.
Subfields: Artificial Intelligence, Information Systems, Machine Translation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Philipp Koehn
- Vishrav Chaudhary
- Francisco (Paco) Guzmán
- Juan Pino
- Huda Khayrallah
- Kenneth Heafield
- Felix Stahlberg
- Holger Schwenk
In Information Systems
In Machine Translation
- Huda Khayrallah
- Philipp Koehn
- Vishrav Chaudhary
- Francisco (Paco) Guzmán
- Juan Pino
- Kenneth Heafield
- Mikel L. Forcada
- Felix Stahlberg
Citations in the corpus
- Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering (2018, background): “AFRL Air Force Research Lab, USA (Erdmann and Gwinnup, 2018) Alibaba Machine Intelligence Technology Lab, Alibaba Group, China (Lu et al., 2018) ARC Inst.”
- Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions (2019, background): “other tokens, matching names, numbers, dates, email addresses, or URLs, too similar sentences (copied content), and language identification (Pinnis, 2018; Lu et al., 2018; Ash et al., 2018).”