We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft).

Achieving a balance between the benefits of biometrics in border processes and the risk that they may cause harm to fundamental rights will require gathering more data about the problems biometrics collection is meant to address, as well as detailed policy considerations, attendees heard in the European Association for Biometrics' (EAB's) virtual lunch talk this week. Bianca-Ioana Marcu of Vrije Universiteit Brussel (VUB) gave a presentation on'Biometrics, Facial Recognition and the Fundamental Rights of Migrants,' focussing on the EU migration management context, and considering non-technical impacts. Marcu noted a tangle of databases that could be involved in EU migration processes, but focussed on the EURODAC database. Biometrics have been adopted in immigration systems to apply efficiency, trust and reliability to the large numbers of people moving between countries, Marcu points out. Pressures on EU external borders, both from migration volumes and terrorism concerns, have resulted in a move towards "the establishment of a genuine security union," she says, facilitated by EU-wide information systems.

We introduce a novel top-down end-to-end formulation of document-level discourse parsing in the Rhetorical Structure Theory (RST) framework. In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions. Our framework facilitates discourse parsing from scratch without requiring discourse segmentation as a prerequisite; rather, it yields segmentation as part of the parsing process. Our unified parsing model adopts a beam search to decode the best tree structure by searching through a space of high-scoring trees. With extensive experiments on the standard English RST discourse treebank, we demonstrate that our parser outperforms existing methods by a good margin in both end-to-end parsing and parsing with gold segmentation. More importantly, it does so without using any handcrafted features, making it faster and easily adaptable to new languages and domains.

We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an $F_1$ score of 95.4, and our parser achieves an $F_1$ score of 81.7 on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 $F_1$).

SDL is suing Silicon Valley startup Lilt, alleging patent infringement. In a lawsuit dated April 3, 2017 and filed in the Northern District of California, SDL said Lilt had violated three of its patents and "continues to interfere" with the marketing and sales of SDL products, threatening SDL's relationships with its customers. The patents referred to in the lawsuit are US patents granted Language Weaver, which SDL had acquired in the summer of 2010. Language Weaver was co-founded in 2002 by Daniel Marcu, who eventually joined SDL as Chief Technology Officer post-acquisition. Marcu went on to become the company's Chief Science Officer before moving to Amazon in December 2016 as Director of Machine Translation and Natural Language Processing.