Abstract
Natural Language Processing has been perplexed for many years by the problem that
multiple semantics are mixed inside a word,
even with the help of context. To solve this
problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model. In the
prism module, some words are selectively replaced with task-related semantic aspects, then
these denoised word representations can be
fed into downstream tasks to make them easier. Besides, we also introduce a structure to
train this module jointly with the downstream
model without additional data. This module
can be easily integrated into the downstream
model and significantly improve the performance of baselines on named entity recognition (NER) task. The ablation analysis demonstrates the rationality of the method. As a side
effect, the proposed method also provides a
way to visualize the contribution of each word.