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Deep Contextualized Word Representations
TLDR
A new type of deep contextualized word representation is introduced that models both complex characteristics of word use and how these uses vary across linguistic contexts, allowing downstream models to mix different types of semi-supervision signals. Expand
AllenNLP: A Deep Semantic Natural Language Processing Platform
TLDR
AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily and provides a flexible data API that handles intelligent batching and padding, and a modular and extensible experiment framework that makes doing good science easy. Expand
Dissecting Contextual Word Embeddings: Architecture and Representation
TLDR
There is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks, suggesting that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated. Expand
Knowledge Enhanced Contextual Word Representations
TLDR
After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation. Expand
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
TLDR
ScispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library is described, which detail the performance of two packages of models released in scispa Cy and demonstrate their robustness on several tasks and datasets. Expand
Ontology alignment in the biomedical domain using entity definitions and context
TLDR
This work proposes a method for enriching entities in an ontology with external definition and context information, and uses this additional information for ontology alignment, and develops a neural architecture capable of encoding the additional information when available. Expand
Grammar-based Neural Text-to-SQL Generation
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar. Grammar-basedExpand
Writing Code for NLP Research
TLDR
This tutorial aims to share best practices for writing code for NLP research, drawing on the instructors' experience designing the recently-released AllenNLP toolkit, a PyTorch-based library for deep learning NLPResearch. Expand
A Deep Semantic Natural Language Processing Platform
TLDR
AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily and provides a flexible data API that handles intelligent batching and padding, and a modular and extensible experiment framework that makes doing good science easy. Expand
Learning to Reason With Adaptive Computation
TLDR
This work introduces the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model. Expand
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