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Neural Motifs: Scene Graph Parsing with Global Context
This work analyzes the role of motifs: regularly appearing substructures in scene graphs and introduces Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graph graphs that improves on the previous state-of-the-art by an average of 3.6% relative improvement across evaluation settings. Expand
VisualBERT: A Simple and Performant Baseline for Vision and Language
Analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments. Expand
QuAC: Question Answering in Context
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
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
A data-augmentation approach is demonstrated that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by rule-based, feature-rich, and neural coreference systems in WinoBias without significantly affecting their performance on existing datasets. Expand
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
This work presents a novel training procedure that can lift the limitation of the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs, and presents strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions. Expand
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
This paper trains a naive model that makes predictions exclusively based on dataset biases, and a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Expand
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
This work proposes to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference to reduce the magnitude of bias amplification in multilabel object classification and visual semantic role labeling. Expand
Situation Recognition: Visual Semantic Role Labeling for Image Understanding
This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participatingExpand
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
  • Mark Yatskar
  • Computer Science, Sociology
  • 27 September 2018
Because of the datasets’ structural similarity, a single extractive model can be easily adapted to any of the dataset and improved baseline results on both SQuAD 2.0 and CoQA are shown. Expand
For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia
This work considers two main approaches to deriving simplification probabilities via an edit model that accounts for a mixture of different operations, and using metadata to focus on edits that are more likely to be simplification operations. Expand