Statistical Translation of Novel Constructions

Natural language translation often seems to proceed by the invocation of fixed constructions like conventional phrases, platitudes and idioms. Indeed, the idiomatic approach is currently the workhorse of state-of-the-art translation models. An input sentence is translated by reciting (with statistics) arbitrary-size phrase pairs from the training data. However, the training data is far from exhaustive and novel constructions are frequent. For novel input, the idiomatic model degrades to composing bilingual bits and pieces independently from one another. If idiomaticity is the rule and training data is never exhaustive, how should novel, previously unseen constructions be translated? It would seem that a compositional approach to translation might actually provide a reasonable approximation for many translation cases. However, earlier experience shows that categorical composition leads to brittle systems with inflated risk of wrong translation choices. The present proposal tackles the challenge of a unified, effective modeling of the whole range of translation phenomena between the apparently compositional and the unquestionably idiomatic. We propose a novel statistical model for machine translation that captures idiomaticity and effective composition as the graded outcomes of the same stochastic process. The project concerns developing this model, exploring the variety of representations involved in its rendering, both theoretically and empirically, and contrasting it to state-of-the-art models on standard benchmark data. Should we succeed to learn compositional and idiomatic translation simultaneously, we will make inroads into an outstanding scientific problem, and lay the cornerstone for building far better translation systems.