Unsupervised Transduction Grammar Induction via Minimum Description Length

We present a minimalist, unsupervised learning model that induces relatively clean phrasal inversion transduction grammars by employing the minimum description length principle to drive search over a space defined by two opposing extreme types of ITGs. In comparison to most current SMT approaches, the model learns a very parsimonious phrase translation lexicons that provide an obvious basis for generalization to abstract translation schemas. To do this, the model maintains internal consistency by avoiding use of mismatched or unrelated models, such as word alignments or probabilities from IBM models. The model introduces a novel strategy for avoiding the pitfalls of premature pruning in chunking approaches, by incrementally splitting an ITGwhile using a second ITG to guide this search.