Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. 1

This paper describes a more precise analysis of punctuation for a bi-directional, broad coverage English grammar extracted from the CCGbank (Hockenmaier and Steedman, 2007). We discuss various approaches which have been proposed in the literature to constrain overgeneration with punctuation, and illustrate how aspects of Briscoe’s (1994) influential approach, which relies on syntactic features to constrain the appearance of balanced and unbalanced commas and dashes to appropriate sentential contexts, is unattractive for CCG. As an interim solution to constrain overgeneration, we propose a rule-based filter which bars illicit sequences of punctuation and cases of improperly unbalanced apposition. Using the OpenCCG toolkit, we demonstrate that our punctuation-augmented grammar yields substantial increases in surface realization coverage and quality, helping to achieve state-of-the-art BLEU scores. 1