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.

[EX Q]: A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.
We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.
[EX A]: A large-scale multi-task learning framework with diverse training objectives to learn fixed-length sentence representations

[EX Q]: The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition. Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence between the representation and modifying the regularization term in the variational lower bound. However recent work by Locatello et al. (2018) has demonstrated that the proposed methods are heavily influenced by randomness and the choice of the hyper-parameter. This work is built upon the same framework in Stage 1 (Li et al., 2019), but with different settings; to make it self-contained, we provide this manuscript, which is unavoidably very similar to the report for Stage 1. In detail, in this work, instead of designing a new regularization term, we adopt the FactorVAE but improve the reconstruction performance and increase the capacity of network and the training step. The strategy turns out to be very effective in achieving disentanglement.
[EX A]: disentangled representation learning

[EX Q]: Recurrent neural networks (RNNs) are widely used to model sequential data but
their non-linear dependencies between sequence elements prevent parallelizing
training over sequence length. We show the training of RNNs with only linear
sequential dependencies can be parallelized over the sequence length using the
parallel scan algorithm, leading to rapid training on long sequences even with
small minibatch size. We develop a parallel linear recurrence CUDA kernel and
show that it can be applied to immediately speed up training and inference of
several state of the art RNN architectures by up to 9x. We abstract recent work
on linear RNNs into a new framework of linear surrogate RNNs and develop a
linear surrogate model for the long short-term memory unit, the GILR-LSTM, that
utilizes parallel linear recurrence. We extend sequence learning to new
extremely long sequence regimes that were previously out of reach by
successfully training a GILR-LSTM on a synthetic sequence classification task
with a one million timestep dependency.

[EX A]:
use parallel scan to parallelize linear recurrent neural nets. train model on length 1 million dependency