In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
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Question: We present a new unsupervised method for learning general-purpose sentence embeddings.
Unlike existing methods which rely on local contexts, such as words
inside the sentence or immediately neighboring sentences, our method selects, for
each target sentence, influential sentences in the entire document based on a document
structure. We identify a dependency structure of sentences using metadata
or text styles. Furthermore, we propose a novel out-of-vocabulary word handling
technique to model many domain-specific terms, which were mostly discarded by
existing sentence embedding methods. We validate our model on several tasks
showing 30% precision improvement in coreference resolution in a technical domain,
and 7.5% accuracy increase in paraphrase detection compared to baselines.

Answer: To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words.

Question: Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and out- put weights, without changing the rest of the network. Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the l2 norm of the weights, equivalently the weight decay regularizer. It provably converges to a unique solution. Interleaving our algorithm with SGD during training improves the test accuracy. For small batches, our approach offers an alternative to batch- and group- normalization on CIFAR-10 and ImageNet with a ResNet-18.

Answer: Fast iterative algorithm to balance the energy of a network while staying in the same functional equivalence class

Question: Autoregressive recurrent neural decoders that generate sequences of tokens one-by-one and left-to-right are the workhorse of modern machine translation. In this work, we propose a new decoder architecture that can generate natural language sequences in an arbitrary order. Along with generating tokens from a given vocabulary, our model additionally learns to select the optimal position for each produced token. The proposed decoder architecture is fully compatible with the seq2seq framework and can be used as a drop-in replacement of any classical decoder. We demonstrate the performance of our new decoder on the IWSLT machine translation task as well as inspect and interpret the learned decoding patterns by analyzing how the model selects new positions for each subsequent token.

Answer:
new out-of-order decoder for neural machine translation