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.

Q: Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible. There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise. Although these frameworks relax the restriction of supervised learning, they are studied independently. Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown. In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt. Under this framework, we compare recent algorithms for semi-supervised and robust learning. The results suggest that semi-supervised learning outperforms robust learning with noisy labels. We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.

A: We propose to compare semi-supervised and robust learning to noisy label under a shared setting
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Q: We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization. ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphrasing complex scientific concepts to be understood by the general audience. For this purpose, we introduce a specialized dataset for ASJ that contains scientific papers and their press releases from Science Daily. While state-of-the-art sequence-to-sequence (seq2seq) models could easily generate convincing press releases for ASJ, these are generally nonfactual and deviate from the source. To address this issue, we improve seq2seq generation via transfer learning by co-training with new targets: (i) scientific abstracts of sources and (ii) partitioned press releases. We further design a measure for factuality that scores how pertinent to the scientific papers the press releases under our seq2seq models are. Our quantitative and qualitative evaluation shows sizable improvements over a strong baseline, suggesting that the proposed framework could improve seq2seq summarization beyond ASJ.

A: New: application of seq2seq modelling to automating sciene journalism; highly abstractive dataset; transfer learning tricks; automatic evaluation measure.
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Q: Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments. This paper proposes reward distribution using {\em Neuron as an Agent} (NaaA) in MARL without a TTP with two key ideas: (i) inter-agent reward distribution and (ii) auction theory. Auction theory is introduced because inter-agent reward distribution is insufficient for optimization. Agents in NaaA maximize their profits (the difference between reward and cost) and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents. NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents. Finally, numerical experiments (a single-agent environment from OpenAI Gym and a multi-agent environment from ViZDoom) confirm that NaaA framework optimization leads to better performance in reinforcement learning.

A:
Neuron as an Agent (NaaA) enable us to train multi-agent communication without a trusted third party.
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