A method is presented for incremental retraining
of an SMT system, in which a local
phrase table is created and incrementally updated
as a file is translated and post-edited.
It is shown that translation data from within
the same file has higher value than other
domain-specific data. In two technical domains,
within-file data increases BLEU score
by several full points. Furthermore, a strong
recency effect is documented; nearby data
within the file has greater value than more
distant data. It is also shown that the value
of translation data is strongly correlated with
a metric defined over new occurrences of ngrams.
Finally, it is argued that the incremental
re-training prototype could serve as the basis
for a practical system which could be interactively
updated in real time in a post-editing
setting. Based on the results here, such an interactive
system has the potential to dramatically
improve translation quality
