Statistical machine translation has made tremendous progress over the past ten
years. The output of even the best systems, however, is often ungrammatical
because of the lack of sufficient linguistic knowledge. Even when systems
incorporate syntax in the translation process, syntactic errors still result. To address
this issue, we present a novel approach for detecting and correcting ungrammatical
translations. In order to simultaneously detect multiple errors and their
corresponding words in a formal framework, we use feature-based lexicalized tree
adjoining grammars, where each lexical item is associated with a syntactic
elementary tree, in which each node is associated with a set of feature-value pairs
to define the lexical item’s syntactic usage. Our syntactic error detection works by
checking the feature values of all lexical items within a sentence using a unification
framework. In order to simultaneously detect multiple error types and track their
corresponding words, we propose a new unification method which allows the
unification procedure to continue when unification fails and also to propagate the
failure information to relevant words. Once error types and their corresponding
words are detected, one is able to correct errors based on a unified consideration of
all related words under the same error types. In this paper, we present some simple
mechanism to handle part of the detected situations. We use our approach to detect
and correct translations of six single statistical machine translation systems. The
results show that most of the corrected translations are improved.