Parallelisation and application of AD3 as a method for solving large scale combinatorial auctions

Auctions, and combinatorial auctions (CAs), have been successfully employed to solve coordination problems in a wide range of application domains. However, the scale of CAs that can be optimally solved is small because of the complexity of the winner determination problem (WDP), namely of finding the bids that maximise the auctioneer’s revenue. A way of approximating the solution of a WDP is to solve its linear programming relaxation. The recently proposed Alternate Direction Dual Decomposition algorithm (AD3) has been shown to ef- ficiently solve large-scale LP relaxations. Hence, in this paper we show how to encode the WDP so that it can be approximated by means of AD3. Moreover, we present PAR-AD3, the first parallel implementation of AD3. PAR-AD3 shows to be up to 12.4 times faster than CPLEX in a single-thread execution, and up to 23 times faster than parallel CPLEX in an 8-core architecture. Therefore PAR- AD3 becomes the algorithm of choice to solve large-scale WDP LP relaxations for hard instances. Furthermore, PAR-AD3 has potential when considering large- scale coordination problems that must be solved as optimisation problems.Research supported by MICINN projects TIN2011-28689-C02-01, TIN2013-45732-C4-4-P and TIN2012-38876-C02-01Peer reviewe

Argument Mining with Structured SVMs and RNNs

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.Comment: Accepted for publication at ACL 2017. 11 pages, 5 figures. Code at https://github.com/vene/marseille and data at http://joonsuk.org

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning.

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge