The model learns to translate using a seq2seq model, an autoencoder objective, and an adversarial objective for language identification.
The system is trained to correct noisy versions of its own output and iteratively improves performance.
Does not require parallel corpora, but relies on a separate method for inducing a parallel dictionary that bootstraps the translation.

https://i.imgur.com/6uXNAgo.png

Updating the parameters in a LSTM language model based on the observed sequence during testing. A slice of text is first processed and then used for a gradient descent update step. A regularisation term is also proposed which draws the parameters back towards the original model.

https://i.imgur.com/zikOowE.png

A toolkit for automatically annotating error correction data with error types. It takes original and corrected sentences as input, aligns them to infer error spans, and uses rules to assign error types. They use the tool to perform fine-grained evaluation of CoNLL-14 shared task participants.

A modular neural architecture for visual question answering. A seq2seq component predicts the sequence of neural modules (eg find() and compare()) based on the textual question, which are then dynamically combined and trained end-to-end. Achieves good results on three separate benchmarks that focus on reasoning about the image.

https://i.imgur.com/iOkSh8y.png

Investigates different parameter choices for encoder-decoder NMT models. They find that LSTM is better than GRU, 2 bidirectional layers is enough, additive attention is the best, and a well-tuned beam search is important. They achieve good results on the WMT15 English->German task and release the code.

https://i.imgur.com/GaAsTvE.png