A Tale about PRO and Monsters

While experimenting with tuning on long sentences, we made an unexpected discovery: that PRO falls victim to monsters – overly long negative examples with very low BLEU+1 scores, which are unsuitable for learning and can cause testing BLEU to drop by several points absolute. We propose several effective ways to address the problem, using lengthand BLEU+1based cut-offs, outlier filters, stochastic sampling, and random acceptance. The best of these fixes not only slay and protect against monsters, but also yield higher stability for PRO as well as improved testtime BLEU scores. Thus, we recommend them to anybody using PRO, monsterbeliever or not. 1 Once Upon a Time... For years, the standard way to do statistical machine translation parameter tuning has been to use minimum error-rate training, or MERT (Och, 2003). However, as researchers started using models with thousands of parameters, new scalable optimization algorithms such as MIRA (Watanabe et al., 2007; Chiang et al., 2008) and PRO (Hopkins and May, 2011) have emerged. As these algorithms are relatively new, they are still not quite well understood, and studying their properties is an active area of research. For example, Nakov et al. (2012) have pointed out that PRO tends to generate translations that are consistently shorter than desired. They have blamed this on inadequate smoothing in PRO’s optimization objective, namely sentencelevel BLEU+1, and they have addressed the problem using more sensible smoothing. We wondered whether the issue could be partially relieved simply by tuning on longer sentences, for which the effect of smoothing would naturally be smaller. To our surprise, tuning on the longer 50% of the tuning sentences had a disastrous effect on PRO, causing an absolute drop of three BLEU points on testing; at the same time, MERT and MIRA did not have such a problem. While investigating the reasons, we discovered hundreds of monsters creeping under PRO’s surface... Our tale continues as follows. We first explain what monsters are in Section 2, then we present a theory about how they can be slayed in Section 3, we put this theory to test in practice in Section 4, and we discuss some related efforts in Section 5. Finally, we present the moral of our tale, and we hint at some planned future battles in Section 6.