Evaluation Order Effects in Dynamic Continuized CCG:
From Negative Polarity Items to Balanced Punctuation

Combinatory Categorial Grammar’s (CCG; Steedman, 2000) flexible treatment of word order and constituency enable it to employ a compact lexicon, an important factor in its successful application to a range of NLP problems. However, its word order flexibility can be problematic for linguistic phenomena where linear order plays a key role. In this talk, I’ll show that the enhanced control over evaluation order afforded by Continuized CCG (Barker & Shan, 2014) makes it possible to formulate improved analyses of negative polarity items and balanced punctuation, and discuss their implementation as a refinement to a prototype parser for Dynamic Continuized CCG (White et al., 2017).

Hypertagging, or supertagging for realization, is the process of assigning CCG tags to predicates. Previous work has shown that it significantly increases realization speed and quality by reducing the search space of the realizer. This project seeks to improve on the current OpenCCG hypertagger, which uses a two-stage maximum entropy algorithm and reaches a dev accuracy of 95.1%. In this talk, I will present the results of various experiments using an LSTM hypertagger with different logical form linearization schemes. The performance with a pre-order linearization scheme is slightly under that of the current OpenCCG hypertagger, but the oracle linearization suggests that with a more English-like linearization, hypertagging with an LSTM is a promising way forward.