• Publications
  • Influence
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
Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets
TLDR
This work introduces inoculation by fine-tuning, a new analysis method for studying challenge datasets by exposing models to a small amount of data from the challenge dataset (a metaphorical pathogen) and assessing how well they can adapt. Expand
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
TLDR
This work presents a new crowdsourced dataset containing more than 24K span-selection questions that require resolving coreference among entities in over 4.7K English paragraphs from Wikipedia, and shows that state-of-the-art reading comprehension models perform significantly worse than humans on this benchmark. Expand
Evaluating Models’ Local Decision Boundaries via Contrast Sets
TLDR
A more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data, and recommends that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Expand
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
TLDR
This work introduces the knowledge graph language model (KGLM), a neural language model with mechanisms for selecting and copying facts from a knowledge graph that are relevant to the context that enable the model to render information it has never seen before, as well as generate out-of-vocabulary tokens. Expand
Crowdsourcing Multiple Choice Science Questions
TLDR
This work presents a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers by leveraging a large corpus of domain-specific text and a small set of existing questions and shows that humans cannot distinguish the crowdsourced questions from original questions. Expand
LSTMs Exploit Linguistic Attributes of Data
TLDR
It is shown that the LSTM learns to solve the memorization task by explicitly using a subset of its neurons to count timesteps in the input, and it is hypothesize that the patterns and structure in natural language data enable LSTMs to learn by providing approximate ways of reducing loss. Expand
Evaluating NLP Models via Contrast Sets
TLDR
A new annotation paradigm for NLP is proposed that helps to close systematic gaps in the test data, and it is recommended that after a dataset is constructed, the dataset authors manually perturb the test instances in small but meaningful ways that change the gold label, creating contrast sets. Expand
Discovering Phonesthemes with Sparse Regularization
TLDR
A simple method for extracting non-arbitrary form-meaning representations from a collection of semantic vectors and applies this model to the problem of automatically discovering phonesthemes, which are submorphemic sound clusters that appear in words with similar meaning. Expand
...
1
2
...