Daniel Hershcovich

A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics

We describe a novel and unique argumentative structure dataset. This corpus consists of data extracted fro m hundreds of Wikipedia articles using a meticulously monitored manual annotation process. The result is 2,683 argument elements, collected in the context of 33 controversial topics, organized under a simp le claim-evidence structure. The obtained data are publicly available for academic research.

Verification of Transactional Memory in POWER8

Transactional memory is a promising mechanism for synchronizing concurrent programs that eliminates locks at the expense of hardware complexity. Transactional memory is a hard feature to verify. First, transactions comprise several instructions that must be observed as a single global atomic operation. In addition, there are many reasons a transaction can fail. This results in a high level of non-determinism which must be tamed by the verification methodology. This paper describes the innovation that was applied to tools and methodology in pre-silicon simulation, acceleration and post-silicon in order to verify transactional memory in the IBM POWER8 processor core.

A Transition-Based Directed Acyclic Graph Parser for UCCA.

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.

Automatic Claim Negation: Why, How and When

The main goal of argumentation mining is to analyze argumentative structures within an argument-rich document, and reason about their composition. Recently, there is also interest in the task of simply detecting claims (sometimes called conclusion) in general documents. In this work we ask how this set of detected claims can be augmented further, by adding to it the negation of each detected claim. This presents two NLP problems: how to automatically negate a claim, and when such a negated claim can plausibly be used. We present first steps into solving both these problems, using a rule-based approach for the former and a statistical one towards the latter.