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Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
TLDR
This paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations and obtain state-of-the-art performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained model.
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss
TLDR
This work presents a novel bi-LSTM model, which combines the POS tagging loss function with an auxiliary loss function that accounts for rare words, which obtains state-of-the-art performance across 22 languages, and works especially well for morphologically complex languages.
On the Limitations of Unsupervised Bilingual Dictionary Induction
TLDR
It is shown that a simple trick, exploiting a weak supervision signal from identical words, enables more robust induction and establishes a near-perfect correlation between unsupervised bilingual dictionary induction performance and a previously unexplored graph similarity metric.
A Survey of Cross-lingual Word Embedding Models
TLDR
A comprehensive typology of cross-lingual word embedding models is provided, showing that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent modulo optimization strategies, hyper-parameters, and such.
Deep multi-task learning with low level tasks supervised at lower layers
TLDR
It is consistently better to have POS supervision at the innermost rather than the outermost layer, and it is argued that “lowlevel” tasks are better kept at the lower layers, enabling the higher- level tasks to make use of the shared representation of the lower-level tasks.
Latent Multi-Task Architecture Learning
TLDR
This work presents an approach that learns a latent multi-task architecture that jointly addresses (a)--(c) and consistently outperforms previous approaches to learning latent architectures for multi- task problems and achieves up to 15% average error reductions over common approaches to MTL.
Multilingual Projection for Parsing Truly Low-Resource Languages
TLDR
This work proposes a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages that consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.
Unsupervised Cross-Lingual Representation Learning
TLDR
This tutorial focuses on how to induce weakly-supervised and unsupervised cross-lingual word representations in truly resource-poor settings where bilingual supervision cannot be guaranteed.
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