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Improving Neural Language Processing with Named Entities
Most neural-based NLP models receive only vectors encoded from a sequence of subwords obtained from an input text.
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing
We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model.
Global Optimization under Length Constraint for Neural Text Summarization
The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6. 70{\%} overlength summaries on CNN/Daily and 7. 8{\%} on long summary of Mainichi, compared to the approximately 20{\%} to 50{\%} on CNN/Daily Mail and 10{\%} to 30{\%} on Mainichi with the other optimization methods.
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