The German question answering (QA) system IRSAW (formerly: InSicht) participated in QA@CLEF for the fifth time. IRSAW was introduced in 2007 by integrating the deep answer producer InSicht, several shallow answer producers, and a logical validator. InSicht builds on a deep QA approach: it transforms documents to semantic representations using a parser, draws inferences on semantic representatio...

In e-commerce portals, generating answers for product-related questions has become a crucial task. this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete from large-scale unlabeled reviews product attributes. However, safe problems (i.e., neural models tend meaningless universal answers) pose significant challenges text tasks, q...

Open forum threads exhibit a great variability in the quality and quantity of the answers they attract, making it difficult to manually moderate and separate relevant from irrelevant content. The goal of SemEval 2015 Task 3 (Subtask A, English) is to build systems that automatically distinguish between relevant and irrelevant content in forum threads. We extend a short answer assessment system ...