CS Seminar: Matt Post – “Putting the “human” in human-parity machine translation”
Hackerman Hall B-17
If you’re a layperson who gets your news from public relations firms
at major industry research centers, you may think that machine
translation is solved, having reached “human parity” sometime in the
past few years. But the reality is quite different. While translation
accuracy is indistinguishable from that of humans by some
definitions in certain narrow settings, claims of human parity rest on
an impoverished definition of human capability. This talk will explore
three lines of work whose collective goal is to provide neural machine
translation systems with a few abilities that come quite naturally to
us but are less natural in the modern translation paradigm, namely:
translating under supplied constraints, producing diverse translation
candidates, and evaluating output more robustly.
Matt Post is a research scientist at the Human Language Technology
Center of Excellence at JHU, with appointments the Department of
Computer Science and at the Center for Language and Speech Processing.
He spends most of his time doing machine translation, but he has also
worked on text classification, grammatical error correction, and human
evaluation, and is interested in most topics in natural language
processing. He is the Director of the ACL Anthology, and for many
years has helped to organize the annual Conference on Machine
Translation (WMT). He spent the 2017–2018 academic year working with
Amazon Research in Berlin.