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.
SOTA for Sarcasm Detection on SCv1 (using extra training data)
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
#15 best model for Aspect-Based Sentiment Analysis on SemEval 2014 Task 4 Sub Task 2
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.
#4 best model for Coreference Resolution on CoNLL 2012
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.
#4 best model for Named Entity Recognition (NER) on Ontonotes v5 (English)