In formal logic-based approaches to Recognizing Textual Entailment (RTE), a
Combinatory Categorial Grammar (CCG) parser is used to parse input premises and
hypotheses to obtain their logical formulas. Here, it is important that the
parser processes the sentences consistently; failing to recognize a similar
syntactic structure results in inconsistent predicate argument structures among
them, in which case the succeeding theorem proving is doomed to failure. In
this work, we present a simple method to extend an existing CCG parser to parse
a set of sentences consistently, which is achieved with an inter-sentence
modeling with Markov Random Fields (MRF). When combined with existing
logic-based systems, our method always shows improvement in the RTE experiments
on English and Japanese languages.