  Inspired by a previous attempt to answer crossword questions using neural
networks (Hill, Cho, Korhonen, & Bengio, 2015), this dissertation implements
extensions to improve the performance of this existing definition model on the
task of answering crossword questions. A discussion and evaluation of the
original implementation finds that there are some ways in which the recurrent
neural model could be extended. Insights from related fields neural language
modeling and neural machine translation provide the justification and means
required for these extensions. Two extensions are applied to the LSTM encoder,
first taking the average of LSTM states across the sequence and secondly using
a bidirectional LSTM, both implementations serve to improve model performance
on a definitions and crossword test set. In order to improve performance on
crossword questions, the training data is increased to include crossword
questions and answers, and this serves to improve results on definitions as
well as crossword questions. The final experiments are conducted using sub-word
unit segmentation, first on the source side and then later preliminary
experimentation is conducted to facilitate character-level output. Initially,
an exact reproduction of the baseline results proves unsuccessful. Despite
this, the extensions improve performance, allowing the definition model to
surpass the performance of the recurrent neural network variants of the
previous work (Hill, et al., 2015).
