• Publications
  • Influence
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
TLDR
K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score. Expand
Transactional storage for geo-replicated systems
TLDR
The design and implementation of Walter is described, a key feature behind Walter is a new property called Parallel Snapshot Isolation (PSI), which allows Walter to replicate data asynchronously, while providing strong guarantees within each site. Expand
Semi-supervised sequence tagging with bidirectional language models
TLDR
A general semi-supervised approach for adding pre- trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers. Expand
Piccolo: Building Fast, Distributed Programs with Partitioned Tables
TLDR
Experiments show Piccolo to be faster than existing data flow models for many problems, while providing similar fault-tolerance guarantees and a convenient programming interface. Expand
Content-Based Citation Recommendation
TLDR
It is shown empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup and released an online portal for citation recommendation based on this method. Expand
Transaction chains: achieving serializability with low latency in geo-distributed storage systems
TLDR
It is shown that it is possible to obtain both serializable transactions and low latency, under two conditions: transactions are known ahead of time, permitting an a priori static analysis of conflicts, and transactions are structured as transaction chains consisting of a sequence of hops. Expand
Extracting Scientific Figures with Distantly Supervised Neural Networks
TLDR
This paper induces high-quality training labels for the task of figure extraction in a large number of scientific documents, with no human intervention, and uses this dataset to train a deep neural network for end-to-end figure detection, yielding a model that can be more easily extended to new domains compared to previous work. Expand
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
TLDR
Explicit Semantic Ranking is introduced, a new ranking technique that leverages knowledge graph embedding that represents queries and documents in the entity space and ranks them based on their semantic connections from their knowledgegraph embedding. Expand
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
TLDR
This model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Expand
...
1
2
3
...