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1. When Bidders Are DAOs

   https://doi.org/10.4230/LIPIcs.AFT.2023.21  

   Bahrani, Maryam; Garimidi, Pranav; Roughgarden, Tim (January 2023, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Bonneau, Joseph; Weinberg, S Matthew (Ed.)

   In a typical decentralized autonomous organization (DAO), people organize themselves into a group that is programmatically managed. DAOs can act as bidders in auctions (with ConstitutionDAO being one notable example), with a DAO’s bid typically treated by the auctioneer as if it had been submitted by an individual, without regard to any details of the internal DAO dynamics. The goal of this paper is to study auctions in which the bidders are DAOs. More precisely, we consider the design of two-level auctions in which the "participants" are groups of bidders rather than individuals. Bidders form DAOs to pool resources, but must then also negotiate the terms by which the DAO’s winnings are shared. We model the outcome of a DAO’s negotiations through an aggregation function (which aggregates DAO members' bids into a single group bid) and a budget-balanced cost-sharing mechanism (that determines DAO members' access to the DAO’s allocation and distributes the aggregate payment demanded from the DAO to its members). DAOs' bids are processed by a direct-revelation mechanism that has no knowledge of the DAO structure (and thus treats each DAO as an individual). Within this framework, we pursue two-level mechanisms that are incentive-compatible (with truthful bidding a dominant strategy for each member of each DAO) and approximately welfare-optimal. We prove that, even in the case of a single-item auction, the DAO dynamics hidden from the outer mechanism preclude incentive-compatible welfare maximization: No matter what the outer mechanism and the cost-sharing mechanisms used by DAOs, the welfare of the resulting two-level mechanism can be a ≈ ln n factor less than the optimal welfare (in the worst case over DAOs and valuation profiles). We complement this lower bound with a natural two-level mechanism that achieves a matching approximate welfare guarantee. This upper bound also extends to multi-item auctions in which individuals have additive valuations. Finally, we show that our positive results cannot be extended much further: Even in multi-item settings in which bidders have unit-demand valuations, truthful two-level mechanisms form a highly restricted class and as a consequence cannot guarantee any non-trivial approximation of the maximum social welfare. 

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2. Document-Level Event Argument Extraction by Conditional Generation

   https://doi.org/10.18653/v1/2021.naacl-main.69  

   Li, Sha; Ji, Heng; Han, Jiawei (June 2021, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021)

   null (Ed.)

   Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WIKIEVENTS which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WIKIEVENTS datasets respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model’s trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE. 

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3. Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation

   https://doi.org/10.18653/v1/2023.findings-acl.662  

   Xu, Zhiyang; Lee, Jay Yoon; Huang, Lifu (August 2023, Association for Computational Linguistics)

   Data scarcity has been the main factor that hinders the progress of event extraction. To overcome this issue, we propose a Self-Training with Feedback (STF) framework that leverages the large-scale unlabeled data and acquires feedback for each new event prediction from the unlabeled data by comparing it to the Abstract Meaning Representation (AMR) graph of the same sentence. Specifically, STF consists of (1) a base event extraction model trained on existing event annotations and then applied to large-scale unlabeled corpora to predict new event mentions as pseudo training samples, and (2) a novel scoring model that takes in each new predicted event trigger, an argument, its argument role, as well as their paths in the AMR graph to estimate a compatibility score indicating the correctness of the pseudo label. The compatibility scores further act as feedback to encourage or discourage the model learning on the pseudo labels during self-training. Experimental results on three benchmark datasets, including ACE05-E, ACE05-E+, and ERE, demonstrate the effectiveness of the STF framework on event extraction, especially event argument extraction, with significant performance gain over the base event extraction models and strong baselines. Our experimental analysis further shows that STF is a generic framework as it can be applied to improve most, if not all, event extraction models by leveraging large-scale unlabeled data, even when high-quality AMR graph annotations are not available. 

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4. Mechanical generation of isocyanate by mechanically induced retro [2 + 2] cycloaddition of a 1,2-diazetidinone mechanophore

   https://doi.org/10.1039/C9QO00262F  

   Lin, Yangju; Chang, Chia-Chih; Craig, Stephen L. (March 2019, Organic Chemistry Frontiers)

   The encapsulation of guests in supramolecular capsules has long been used to trap reactive intermediates and enhance or reduce the kinetic stability of reactants, and alter the products of chemical reactions that occur within the capsule interior. In recent years, multiple studies have shown that variations of normal reactivity patterns can be induced by trapping reactants under tension, for example along a backbone of an overextended polymer chain, in a manner that is fundamentally very different from, but reminiscent of, encapsulation. Here, we describe the formation of a mechanochemically generated isocyante via a mechanical retro [2 + 2] cycloaddition of a 1,2-diazetidinone (DAO) mechanophore. A single DAO mechanophore is incorporated into the chain center of a poly(methyl acrylate) (PMA) backbone via single electron transfer-living radical polymerization (SET-LRP). Mechanical activation of the DAO via ultrasonic sonication leads to the formation of isocyanate and imine products, as supported by trapping experiments using 9-(methylaminomethyl)anthracene labelling and 1 H NMR spectroscopy. Further, we examine the relative mechanical susceptibility of chain-centered DAO mechanophores through a variety of methods, and evaluate the advantage and disadvantage of each. 

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5. Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

   https://doi.org/10.1145/3178876.3186024  

   Qu, Meng; Ren, Xiang; Zhang, Yu; Han, Jiawei (April 2018, Proceedings of the 2018 World Wide Web Conference on World Wide Web, {WWW} 2018,)

   Extracting relations from text corpora is an important task with wide applications. However, it becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, utilizing a few relation instances (i.e., a pair of entities and their relation) as seeds to extract from corpora more instances of the same relation. Existing distributional approaches leverage the corpuslevel co-occurrence statistics of entities to predict their relations, and require a large number of labeled instances to learn effective relation classiffers. Alternatively, pattern-based approaches perform boostrapping or apply neural networks to model the local contexts, but still rely on a large number of labeled instances to build reliable models. In this paper, we study the integration of distributional and pattern-based methods in a weakly-supervised setting such that the two kinds of methods can provide complementary supervision for each other to build an effective, uniffed model. We propose a novel co-training framework with a distributional module and a pattern module. During training, the distributional module helps the pattern module discriminate between the informative patterns and other patterns, and the pattern module generates some highly-conffdent instances to improve the distributional module. The whole framework can be effectively optimized by iterating between improving the pattern module and updating the distributional module. We conduct experiments on two tasks: knowledge base completion with text corpora and corpus-level relation extraction. Experimental results prove the effectiveness of our framework over many competitive baselines. 

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