In the last twenty years the European energy security debate has been overwhelmingly focused on the energy security of the Central and Eastern European (CEE) states and on their relationship with Russia. The key issue has been Russia's attempt to maintain its influence and control through its energy dependencies. The article argues that the problems encountered by the CEE states have been resolved to a large extent due to the actions taken by the European Commission (EC). The EC has played a vital role in providing a collective voice for CEE and in supporting their regional energy integration in order to reduce their vulnerability to external pressures from Russia. The article goes on to argue that while the issue of CEE energy dilemmas has been addressed this does not, however, mean that the European energy predicament regarding Russia has been fully resolved. The article contends that the European-Russian energy relationship is also conditioned by larger geopolitical concerns regarding the US-Russian relationship, NATO, as well as by Cold War legacies which have come to the surface in a cyclical manner. The Trump administration's sharp criticism of new energy infrastructure projects constructed from Europe to Russia underscores this point. The article asserts that European energy security will remain problematic in the absence of a more general political settlement between Russia and the West.
Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set of connected evidence pieces leads to the final verdict of a claim. However, existing datasets either do not provide annotations for gold evidence pages, or the only dataset which does (FEVER) mostly consists of claims which can be fact-checked with simple reasoning and is constructed artificially. Here, we study more complex claim verification of naturally occurring claims with multiple hops over interconnected evidence chunks. We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex and achieve the best performance with an architecture that specifically models reasoning over evidence pieces in combination with in-domain transfer learning.
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