Pointing the Unknown Words
This addresses a key issue for NLP systems like translation and summarization, though it is an incremental improvement over existing attention-based methods.
The paper tackles the problem of rare and unknown words in NLP by proposing a neural model that uses attention and two softmax layers to predict words adaptively, showing improvements in neural machine translation on Europarl and text summarization on Gigaword.
The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the deep learning models. We propose a novel way to deal with the rare and unseen words for the neural network models using attention. Our model uses two softmax layers in order to predict the next word in conditional language models: one predicts the location of a word in the source sentence, and the other predicts a word in the shortlist vocabulary. At each time-step, the decision of which softmax layer to use choose adaptively made by an MLP which is conditioned on the context.~We motivate our work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.~We observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset using our proposed model.