Search Results for author:
Found 9 papers, 4 papers with code
Image-to-Markup Generation with Coarse-to-Fine Attention
We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism.
Fusion of Detected Objects in Text for Visual Question Answering
To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language.
Matching the Blanks: Distributional Similarity for Relation Learning
General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.
Ranked #9 on
Relation Classification
on TACRED
Scene Transformer: A unified architecture for predicting multiple agent trajectories
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Coarse-to-Fine Attention Models for Document Summarization
Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization.
Ranked #25 on
Document Summarization
on CNN / Daily Mail
Learning Cross-Context Entity Representations from Text
Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.
Ranked #1 on
Entity Linking
on CoNLL-Aida
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories
Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases.
KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction
Predicting future trajectories of road agents is a critical task for autonomous driving.
