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SQuAD: 100,000+ Questions for Machine Comprehension of Text
TLDR
A strong logistic regression model is built, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). Expand
Know What You Don’t Know: Unanswerable Questions for SQuAD
TLDR
SQuadRUn is a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. Expand
Semantic Parsing on Freebase from Question-Answer Pairs
TLDR
This paper trains a semantic parser that scales up to Freebase and outperforms their state-of-the-art parser on the dataset of Cai and Yates (2013), despite not having annotated logical forms. Expand
Understanding Black-box Predictions via Influence Functions
TLDR
This paper uses influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Expand
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer
TLDR
This paper proposes simpler methods motivated by the observation that text attributes are often marked by distinctive phrases, and the strongest method extracts content words by deleting phrases associated with the sentence’s original attribute value, retrieves new phrases associatedwith the target attribute, and uses a neural model to fluently combine these into a final output. Expand
QuAC: Question Answering in Context
TLDR
QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation. Expand
Adversarial Examples for Evaluating Reading Comprehension Systems
TLDR
This work proposes an adversarial evaluation scheme for the Stanford Question Answering Dataset that tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences without changing the correct answer or misleading humans. Expand
Unlabeled Data Improves Adversarial Robustness
TLDR
It is proved that unlabeled data bridges the complexity gap between standard and robust classification: a simple semisupervised learning procedure (self-training) achieves high robust accuracy using the same number of labels required for achieving high standard accuracy. Expand
Certified Defenses against Adversarial Examples
TLDR
This work proposes a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value, providing an adaptive regularizer that encourages robustness against all attacks. Expand
Dropout Training as Adaptive Regularization
TLDR
By casting dropout as regularization, this work develops a natural semi-supervised algorithm that uses unlabeled data to create a better adaptive regularizer and consistently boosts the performance of dropout training, improving on state-of-the-art results on the IMDB reviews dataset. Expand
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