Improved Neural Relation Detection for Knowledge Base Question Answering

Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.Comment: Accepted by ACL 2017 (updated for camera-ready

LCT-MALTAs submission to RepEval 2017 shared task

We present in this paper our team LCTMALTA’s submission to the RepEval 2017 Shared Task on natural language inference. Our system is a simple system based on a standard BiLSTM architecture, using as input GloVe word embeddings augmented with further linguistic information. We use max pooling on the BiLSTM outputs to obtain embeddings for sentences. On both the matched and the mismatched test sets, our system clearly beats the shared task’s BiLSTM baseline model.peer-reviewe