Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA).In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question.Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different levels of abstraction.Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to make the two components enhance each other.Our experimental results show that our approach not only achieves outstanding relation detection performance, but more importantly, it helps our KBQA system achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.

While automatic keyphrase extraction has been examined extensively, state-of-the-art performance on this task is still much lower than that on many core natural lan-guage processing tasks. We present a sur-vey of the state of the art in automatic keyphrase extraction, examining the major sources of errors made by existing systems and discussing the challenges ahead. 1

Recent years have seen a surge of interest in stance classification in online debates.Oftentimes, however, it is important to determine not only the stance expressed by an author in her debate posts, but also the reasons behind her supporting or opposing the issue under debate.We therefore examine the new task of reason classification in this paper.Given the close interplay between stance classification and reason classification, we design computational models for examining how automatically computed stance information can be profitably exploited for reason classification.Experiments on our reason-annotated corpus of ideological debate posts from four domains demonstrate that sophisticated models of stances and reasons can indeed yield more accurate reason and stance classification results than their simpler counterparts.