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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
Linguistic Knowledge and Transferability of Contextual Representations
TLDR
It is found that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge. Expand
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
TLDR
A new reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs, and presents a new model that combines reading comprehension methods with simple numerical reasoning to achieve 51% F1. Expand
Never-Ending Learning
TLDR
The Never-Ending Language Learner is described, which achieves some of the desired properties of a never-ending learner, and lessons learned are discussed. Expand
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce wellExpand
Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction
TLDR
It is shown that the random walk probabilities computed by PRA provide no discernible benefit to performance on this task, so they can safely be dropped, and this allows a simpler algorithm for generating feature matrices from graphs, which is called subgraph feature extraction (SFE). Expand
Universal Adversarial Triggers for Attacking and Analyzing NLP
Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger aExpand
Compositional Questions Do Not Necessitate Multi-hop Reasoning
TLDR
This work introduces a single-hop BERT-based RC model that achieves 67 F1—comparable to state-of-the-art multi-hop models and designs an evaluation setting where humans are not shown all of the necessary paragraphs for the intendedmulti-hop reasoning but can still answer over 80% of questions. Expand
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
TLDR
A new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables with a state-of-the-art accuracy and type constraints and entity linking are valuable components to incorporate in neural semantic parsers. Expand
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