  We build a dual-way neural dictionary to retrieve words given definitions,
and produce definitions for queried words. The model learns the two tasks
simultaneously and handles unknown words via embeddings. It casts a word or a
definition to the same representation space through a shared layer, then
generates the other form in a multi-task fashion. Our method achieves promising
automatic scores on previous benchmarks without extra resources. Human
annotators prefer the model's outputs in both reference-less and
reference-based evaluation, indicating its practicality. Analysis suggests that
multiple objectives benefit learning.
