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The papers are not selected or ordered based on any criteria. https://arxiv.org/pdf/1606.02858Hermann et al (2015) created a dataset for testing reading comprehension by extracting summarised bullet points from CNN and Daily Mail.It is not a list of the best papers I have read, more like a random sample. All the entities in the text are anonymised and the task is to place correct entities into empty slots based on the news article.
But I do try to present the crux of the paper as bluntly as possible, without unnecessary sales tactics.
Hopefully this can give you the general idea of 50 papers, in roughly 20 minutes of reading time.
Here, they include a ranking SVM to score and reorder the n-best lists from the translation model.
The reranking features include various internal scores from the translation model, the rank in the original ordering, language model probabilities trained on large corpora, language model scores based on only the n-best list, word-level translation probabilities, and sentence length features. https://arxiv.org/abs/1605.07869 They start with the neural machine translation model using alignment, by Bahdanau et al. The authors use two neural variational components to model a distribution over latent variables z that captures the semantics of a sentence being translated.
A strategy for an unsupervised stopping criterion is also proposed. A Nested Attention Neural Hybrid Model for Grammatical Error Correction Jianshu Ji, Qinlong Wang, Kristina Toutanova, Yongen Gong, Steven Truong, Jianfeng Gao. character-based extensions to a neural MT system for grammatical error correction.
OOV words are represented in the encoder and decoder using character-based RNNs. goal is to improve the training process for a spoken dialogue system, more specifically a telephone-based system providing restaurant information for the Cambridge (UK) area.
They show improvement on two error correction datasets. Variational Neural Machine Translation Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang. First, they model the posterior probability of z, conditioned on both input and output.
Then they also model the prior of z, conditioned only on the input.
They also propose two modifications to the process of generating adversarial images – making it into a more gradual iterative process, and optimising for a specific adversarial class. Extracting token-level signals of syntactic processing from f MRI – with an application to POS induction Joachim Bingel, Maria Barrett, Anders Søgaard. For this they use a dataset of f MRI recordings, where the subjects were reading a chapter of Harry Potter.
The main issue is that f MRI has very low temporal resolution – there is only one f MRI reading per 4 tokens, and in general it takes around 4-14 seconds for something to show up in f MRI. propose a joint model for 1) identifying event keywords in a text, 2) identifying entities, and 3) identifying the connections between these events and entities.