  Word-level AutoCompletion(WLAC) is a rewarding yet challenging task in
Computer-aided Translation. Existing work addresses this task through a
classification model based on a neural network that maps the hidden vector of
the input context into its corresponding label (i.e., the candidate target word
is treated as a label). Since the context hidden vector itself does not take
the label into account and it is projected to the label through a linear
classifier, the model can not sufficiently leverage valuable information from
the source sentence as verified in our experiments, which eventually hinders
its overall performance. To alleviate this issue, this work proposes an
energy-based model for WLAC, which enables the context hidden vector to capture
crucial information from the source sentence. Unfortunately, training and
inference suffer from efficiency and effectiveness challenges, thereby we
employ three simple yet effective strategies to put our model into practice.
Experiments on four standard benchmarks demonstrate that our reranking-based
approach achieves substantial improvements (about 6.07%) over the previous
state-of-the-art model. Further analyses show that each strategy of our
approach contributes to the final performance.
