  We present a graph-based Tree Adjoining Grammar (TAG) parser that uses
BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end
parser, which jointly performs supertagging, POS tagging, and parsing,
outperforms the previously reported best results by more than 2.2 LAS and UAS
points. The graph-based parsing architecture allows for global inference and
rich feature representations for TAG parsing, alleviating the fundamental
trade-off between transition-based and graph-based parsing systems. We also
demonstrate that the proposed parser achieves state-of-the-art performance in
the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and
Unbounded Dependency Recovery. This provides further support for the claim that
TAG is a viable formalism for problems that require rich structural analysis of
sentences.
