Yui Suzuki, Tomoyuki Kajiwara, Mamoru Komachi Experiments on popular datasets (KITTI 2012, KITTI 2015 and Middlebury 2014) and other challenging test images demonstrate the effectiveness of our proposal.īuilding a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation Systems Thus, we train the network based on a novel loss-function that penalizes predictions disagreeing with the highly confident disparities provided by the algorithm and enforces a smoothness constraint. We rely on off-the-shelf stereo algorithms together with state-of-the-art confidence measures, the latter able to ascertain upon correctness of the measurements yielded by former. In this paper we propose a novel unsupervised adaptation approach that enables to fine-tune a deep learning stereo model without any ground-truth information. Yet, besides a few public datasets such as Kitti, the ground-truth needed to adapt the network to a new scenario is hardly available in practice. Computer generated imagery is deployed to gather the large data corpus required to train such networks, an additional fine-tuning allowing to adapt the model to work well also on real and possibly diverse environments. Recent ground-breaking works have shown that deep neural networks can be trained end-to-end to regress dense disparity maps directly from image pairs. Language Modelling, Semantic Textual Similarity, Word EmbeddingsĪlessio Tonioni, Matteo Poggi, Stefano Mattoccia, Luigi Di Stefano We use a simple forward-feeding neural network for this task and achieve an accuracy of 0.77 which is comparable to the accuracy obtained using 64k features and a support vector machine by Habernal and Gurevych.įinnish resources for evaluating language model semantics Rather than using argument specific feature values, we measure feature values relative to the average value in the debate, allowing us to determine argument convincingness with fewer features (between 5 and 35) than normally used for natural language processing tasks. We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016). Lisa Andreevna Chalaguine, Claudia Schulz An analysis of the probabilistic models constructed shows that representation-building allows MOSES to exploit linkages in solving these problems.Īssessing Convincingness of Arguments in Online Debates with Limited Number of Features Application of MOSES to solve the artificial ant and hierarchically composed parity-multiplexer problems is described, with results showing superior performance. program subspaces) that are maintained simultaneously and managed by the overall system. This leads to a system of demes consisting of alternative representations (i.e. Rather, a novel representation-building procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. Distributions are not estimated over the entire space of programs. I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Meta-Optimizing Semantic Evolutionary Search Unsupervised Adaptation for Deep … A modernised version of the Glossa corpus search systemĪ modernised version of the Glossa corpus search systemĪnders N Kosek, Joel PriestleyĪikaterini-Lida Kalouli, Valeria de Paiva, Livy RealĬommon Sense Reasoning, Natural Language Inference, Semantic Textual Similarity Finnish resources for evaluating language model semantics. Assessing Convincingness of Arguments in Online Debates with Limited Number of Features. Meta-Optimizing Semantic Evolutionary Search. A modernised version of the Glossa corpus search system.

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