Search Results for author:
Found 17 papers, 6 papers with code
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.
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
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.
A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
We address fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages.
State-of-the-art Chinese Word Segmentation with Bi-LSTMs
A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation.
Adversarial Neural Networks for Cross-lingual Sequence Tagging
We study cross-lingual sequence tagging with little or no labeled data in the target language.
Linguistically-Informed Self-Attention for Semantic Role Labeling
Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.
Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models.
SyntaxNet Models for the CoNLL 2017 Shared Task
We describe a baseline dependency parsing system for the CoNLL2017 Shared Task.
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.
Stack-propagation: Improved Representation Learning for Syntax
Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates.
Globally Normalized Transition-Based Neural Networks
Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models.
Ranked #16 on
Dependency Parsing
on Penn Treebank
Structured Training for Neural Network Transition-Based Parsing
We present structured perceptron training for neural network transition-based dependency parsing.
Ranked #17 on
Dependency Parsing
on Penn Treebank
Understanding Objects in Detail with Fine-Grained Attributes
We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object.
SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning
We propose SCALPEL, a flexible method for object segmentation that integrates rich region-merging cues with midand high-level information about object layout, class, and scale into the segmentation process.
Sidestepping Intractable Inference with Structured Ensemble Cascades
For many structured prediction problems, complex models often require adopting approximate inference techniques such as variational methods or sampling, which generally provide no satisfactory accuracy guarantees.
