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.1

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains.Basically, OONP reads a document and parses it into a predesigned object-oriented data structure that reflects the domain-specific semantics of the document.An OONP parser models semantic parsing as a decision process: a neural netbased Reader sequentially goes through the document, and builds and updates an intermediate ontology during the process to summarize its partial understanding of the text.OONP supports a big variety of forms (both symbolic and differentiable) for representing the state and the document, and a rich family of operations to compose the representation.An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two.Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.* The work was done when these authors worked as interns at DeeplyCurious.ai.