Neural Machine Translation (NMT) has shown remarkable progress over the past few years with production systems now being deployed to end-users.
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.
Named-entity recognition (NER) aims at identifying entities of interest in a text.
NLP tasks are often limited by scarcity of manually annotated data.
Ranked #1 on Sarcasm Detection on SCv1 (using extra training data)
The performance of Neural Machine Translation (NMT) models relies heavily on the availability of sufficient amounts of parallel data, and an efficient and effective way of leveraging the vastly available amounts of monolingual data has yet to be found.
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.
We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector.
Ranked #1 on Coreference Resolution on CoNLL 2012