What can Statistical Machine Translation teach Neural Text Generation about Optimization? Graham Neubig @ NAACL Workshop on Methods for Optimizing and Evaluating Neural Language Generation 6/6/2019
or How to Optimize your Neural Generation System towards your Evaluation Function Graham Neubig @ NAACL Workshop on Methods for Optimizing and Evaluating Neural Language Generation 6/6/2019
... Neubig & Watanabe, Computational Linguistics (2016)
<latexit sha1_base64="zi4lDHl42mhk2a3gk9P95mU898=">ACG3icbZDLSgMxFIYz9VbrbdSlm2ARWpA6o4K6EIpuXFawtrVThkwm04YmSHJCGXog7jxVdy4UHEluPBtTC8Lrf4Q+PjPOUnOHySMKu04X1Zubn5hcSm/XFhZXVvfsDe3blWcSkzqOGaxbAZIEUYFqWuqGWkmkiAeMNI+pejeuOeSEVjcaMHCelw1BU0ohpY/n2kcfiLqyVWtDjNITNMjyHnkq5T6HzDUhGlHSoz4tNfdbZXgA73y76FScseBfcKdQBFPVfPvDC2OciI0ZkiptuskupMhqSlmZFjwUkUShPuoS9oGBeJEdbLxckO4Z5wQRrE0R2g4dn9OZIgrNeCB6eRI9RsbWT+V2unOjrtZFQkqSYCTx6KUgZ1DEdJwZBKgjUbGEBYUvNXiHtIqxNngUTgju78l+oH1bOKu71cbF6MU0jD3bALigBF5yAKrgCNVAHGDyAJ/ACXq1H69l6s94nrTlrOrMNfsn6/AYeVZ56</latexit> <latexit sha1_base64="zi4lDHl42mhk2a3gk9P95mU898=">ACG3icbZDLSgMxFIYz9VbrbdSlm2ARWpA6o4K6EIpuXFawtrVThkwm04YmSHJCGXog7jxVdy4UHEluPBtTC8Lrf4Q+PjPOUnOHySMKu04X1Zubn5hcSm/XFhZXVvfsDe3blWcSkzqOGaxbAZIEUYFqWuqGWkmkiAeMNI+pejeuOeSEVjcaMHCelw1BU0ohpY/n2kcfiLqyVWtDjNITNMjyHnkq5T6HzDUhGlHSoz4tNfdbZXgA73y76FScseBfcKdQBFPVfPvDC2OciI0ZkiptuskupMhqSlmZFjwUkUShPuoS9oGBeJEdbLxckO4Z5wQRrE0R2g4dn9OZIgrNeCB6eRI9RsbWT+V2unOjrtZFQkqSYCTx6KUgZ1DEdJwZBKgjUbGEBYUvNXiHtIqxNngUTgju78l+oH1bOKu71cbF6MU0jD3bALigBF5yAKrgCNVAHGDyAJ/ACXq1H69l6s94nrTlrOrMNfsn6/AYeVZ56</latexit> <latexit sha1_base64="zi4lDHl42mhk2a3gk9P95mU898=">ACG3icbZDLSgMxFIYz9VbrbdSlm2ARWpA6o4K6EIpuXFawtrVThkwm04YmSHJCGXog7jxVdy4UHEluPBtTC8Lrf4Q+PjPOUnOHySMKu04X1Zubn5hcSm/XFhZXVvfsDe3blWcSkzqOGaxbAZIEUYFqWuqGWkmkiAeMNI+pejeuOeSEVjcaMHCelw1BU0ohpY/n2kcfiLqyVWtDjNITNMjyHnkq5T6HzDUhGlHSoz4tNfdbZXgA73y76FScseBfcKdQBFPVfPvDC2OciI0ZkiptuskupMhqSlmZFjwUkUShPuoS9oGBeJEdbLxckO4Z5wQRrE0R2g4dn9OZIgrNeCB6eRI9RsbWT+V2unOjrtZFQkqSYCTx6KUgZ1DEdJwZBKgjUbGEBYUvNXiHtIqxNngUTgju78l+oH1bOKu71cbF6MU0jD3bALigBF5yAKrgCNVAHGDyAJ/ACXq1H69l6s94nrTlrOrMNfsn6/AYeVZ56</latexit> Then: Symbolic Translation Models kono eiga kirai ga this movie I hate • First step: learn component models to maximize likelihood • Translation model P(y|x) -- e.g. P( movie | eiga ) • Language model P(Y) -- e.g. P(hate | I) • Reordering model -- e.g. P(<swap> | eiga, ga kirai ) • Length model P(|Y|) -- e.g. word penalty for each word added • Second step: learning log-linear combination to maximize translation accuracy [Och 2004] X log P ( Y | X ) = λ i φ i ( X, Y ) /Z i Minimum Error Rate Training in Statistical Machine Translation (Och 2004)
Now: Auto-regressive Neural Networks kono eiga ga kirai Encoder </s> I hate this movie Decoder dec dec dec dec </s> I hate this movie • All parameters trained end-to-end, usually to maximize likelihood (not accuracy!)
Standard MT System Training/Decoding
<latexit sha1_base64="4+Z5A9vFnGki2tmcH1tEn43Xra8=">ACKXicbVDLSsNAFJ34tr6qLt0MFqGClkQEdSH4QHFZwarQ1DCZ3OrgJBNmboQS8j1u/BU3Lnxt/RGnNQtfBy6cOede5t4TplIYdN03Z2h4ZHRsfGKyMjU9MztXnV84NyrTHFpcSaUvQ2ZAigRaKFDCZaqBxaGEi/D2sO9f3IE2QiVn2EuhE7PrRHQFZ2iloLrfrB9RPxYRPV6lu9RPtYqCHe94io/K2izDgGW/hqFwFujvowUmv4jx3WvWA2qNbfhDkD/Eq8kNVKiGVSf/EjxLIYEuWTGtD03xU7ONAouoaj4mYGU8Vt2DW1LExaD6eSDUwu6YpWIdpW2lSAdqN8nchYb04tD2xkzvDG/vb74n9fOsLvdyUWSZgJ/qom0mKivZzo5HQwFH2LGFcC7sr5TdM423YoNwft98l/S2mjsNLzTzdreQZnGBFkiy6ROPLJF9sgJaZIW4eSePJn8uI8OE/Oq/P+1TrklDOL5Aecj0/UeaN6</latexit> <latexit sha1_base64="4+Z5A9vFnGki2tmcH1tEn43Xra8=">ACKXicbVDLSsNAFJ34tr6qLt0MFqGClkQEdSH4QHFZwarQ1DCZ3OrgJBNmboQS8j1u/BU3Lnxt/RGnNQtfBy6cOede5t4TplIYdN03Z2h4ZHRsfGKyMjU9MztXnV84NyrTHFpcSaUvQ2ZAigRaKFDCZaqBxaGEi/D2sO9f3IE2QiVn2EuhE7PrRHQFZ2iloLrfrB9RPxYRPV6lu9RPtYqCHe94io/K2izDgGW/hqFwFujvowUmv4jx3WvWA2qNbfhDkD/Eq8kNVKiGVSf/EjxLIYEuWTGtD03xU7ONAouoaj4mYGU8Vt2DW1LExaD6eSDUwu6YpWIdpW2lSAdqN8nchYb04tD2xkzvDG/vb74n9fOsLvdyUWSZgJ/qom0mKivZzo5HQwFH2LGFcC7sr5TdM423YoNwft98l/S2mjsNLzTzdreQZnGBFkiy6ROPLJF9sgJaZIW4eSePJn8uI8OE/Oq/P+1TrklDOL5Aecj0/UeaN6</latexit> <latexit sha1_base64="4+Z5A9vFnGki2tmcH1tEn43Xra8=">ACKXicbVDLSsNAFJ34tr6qLt0MFqGClkQEdSH4QHFZwarQ1DCZ3OrgJBNmboQS8j1u/BU3Lnxt/RGnNQtfBy6cOede5t4TplIYdN03Z2h4ZHRsfGKyMjU9MztXnV84NyrTHFpcSaUvQ2ZAigRaKFDCZaqBxaGEi/D2sO9f3IE2QiVn2EuhE7PrRHQFZ2iloLrfrB9RPxYRPV6lu9RPtYqCHe94io/K2izDgGW/hqFwFujvowUmv4jx3WvWA2qNbfhDkD/Eq8kNVKiGVSf/EjxLIYEuWTGtD03xU7ONAouoaj4mYGU8Vt2DW1LExaD6eSDUwu6YpWIdpW2lSAdqN8nchYb04tD2xkzvDG/vb74n9fOsLvdyUWSZgJ/qom0mKivZzo5HQwFH2LGFcC7sr5TdM423YoNwft98l/S2mjsNLzTzdreQZnGBFkiy6ROPLJF9sgJaZIW4eSePJn8uI8OE/Oq/P+1TrklDOL5Aecj0/UeaN6</latexit> Decoder Structure encoder I hate this movie classify classify classify classify classify I hate this movie </s> T Y P ( E | F ) = P ( e t | F, e 1 , . . . , e t − 1 ) t =1
<latexit sha1_base64="GeA/Os4/BK6Zz954iZvfPtPrQE=">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</latexit> <latexit sha1_base64="GeA/Os4/BK6Zz954iZvfPtPrQE=">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</latexit> <latexit sha1_base64="GeA/Os4/BK6Zz954iZvfPtPrQE=">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</latexit> Maximum Likelihood Training • Maximum the likelihood of predicting the next word in the reference given the previous words ` ( E | F ) = − log P ( E | F ) T X = − log P ( e t | F, e 1 , . . . , e t − 1 ) t =1 • Also called "teacher forcing"
Problem 1: Exposure Bias • Teacher forcing assumes feeding correct previous input, but at test time we may make mistakes that propagate encoder I I I I classify classify classify classify classify I I I I I • Exposure bias: The model is not exposed to mistakes during training, and cannot deal with them at test • Really important! One main source of commonly witnessed phenomena such as repeating.
Problem 2: Disregard to Evaluation Metrics • In the end, we want good translations • Good translations can be measured with metrics, e.g. BLEU or METEOR • Really important! Causes systematic problems: • Hypothesis-reference length mismatch • Dropped/repeated content
A Clear Example • My (winning) submission to Workshop on Asian Translation 2016 [Neubig 16] Length Ratio BLEU 100 27 95 26 90 25 85 24 80 23 MLE MLE+Length MinRisk MLE MLE+Length MinRisk • Just training for (sentence-level) BLEU largely fixes length problems, and does much better than heuristics Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016 (Neubig 16)
Error and Risk
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