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: In this paper, we propose a differentiable adversarial grammar model for future prediction. The objective is to model a formal grammar in terms of differentiable functions and latent representations, so that their learning is possible through standard backpropagation. Learning a formal grammar represented with latent terminals, non-terminals, and productions rules allows capturing sequential structures with multiple possibilities from data.

 The adversarial grammar is designed so that it can learn stochastic production rules from the data distribution. Being able to select multiple production rules leads to different predicted outcomes, thus efficiently modeling many plausible futures.   We confirm the benefit of the adversarial grammar on two diverse tasks: future 3D human pose prediction and future activity prediction. For all settings, the proposed adversarial grammar outperforms the state-of-the-art approaches, being able to predict much more accurately and further in the future, than prior work.

Answer: We design a grammar that is learned in an adversarial setting and apply it to future prediction in video.


Question: We investigate methods for semi-supervised learning (SSL) of a neural linear-chain conditional random field (CRF) for Named Entity Recognition (NER) by treating the tagger as the amortized variational posterior in a generative model of text given tags. We first illustrate how to incorporate a CRF in a VAE, enabling end-to-end training on semi-supervised data. We then investigate a series of increasingly complex deep generative models of tokens given tags enabled by end-to-end optimization, comparing the proposed models against supervised and strong CRF SSL baselines on the Ontonotes5 NER dataset. We find that our best proposed model consistently improves performance by $\approx 1\%$ F1 in low- and moderate-resource regimes and easily addresses degenerate model behavior in a more difficult, partially supervised setting.

Answer: We embed a CRF in a VAE of tokens and NER tags for semi-supervised learning and show improvements in low-resource settings.


Question: In this work, we first conduct mathematical analysis on the memory, which is
 defined as a function that maps an element in a sequence to the current output,
 of three RNN cells; namely, the simple recurrent neural network (SRN), the long
 short-term memory (LSTM) and the gated recurrent unit (GRU). Based on the
 analysis, we propose a new design, called the extended-long short-term memory
 (ELSTM), to extend the memory length of a cell. Next, we present a multi-task
 RNN model that is robust to previous erroneous predictions, called the dependent
 bidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout
 (SISO) problem. Finally, the performance of the DBRNN model with the
 ELSTM cell is demonstrated by experimental results.

Answer:
A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems