Squibs and Discussions: Measuring Word Alignment Quality for Statistical Machine Translation
By Alexander M. Fraser, Daniel Marcu. 2007
107 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks
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
- Christopher D. Manning
- Jianfeng Gao
- Graham Neubig
- Mohit Bansal
- Bill MacCartney
- Yoav Goldberg
- Philipp Koehn
- Luke S. Zettlemoyer
In Natural Language Processing
- Christopher D. Manning
- Bill MacCartney
- Samuel R. Bowman
- Graham Neubig
- Mohit Bansal
- Jianfeng Gao
- Michel Galley
- Philipp Koehn
In Evaluation & Benchmarks
- Christopher D. Manning
- Marcin Junczys-Dowmunt
- Arkadiusz Szal
- Michel Galley
- Bill MacCartney
- Mohit Bansal
- Yonatan Belinkov
- Douwe Kiela
Citations in the corpus
- Effective Approaches to Attention-based Neural Machine Translation (2015, neutral): “The AER obtained by the ensemble, while good, is not better than the local-m AER, suggesting the well-known observation that AER and translation scores are not well correlated (Fraser and Marcu, 2007).”
- SyMGiza++: Symmetrized Word Alignment Models for Statistical Machine Translation (2011, background): “2 Machine Translation Results We agree with [11] that the evaluation of alignment quality on its own may not be very meaningful.”
- A Phrase-Based Alignment Model for Natural Language Inference (2008, methodology): “As a baseline, we use a simple alignment algorithm inspired by the lexical entailment model of Glickman et al. (2005), and similar to the simple heuristic model described in ( Och and Ney, 2003 ).”