Wasserstein barycenter model ensembling
Pierre Dognin, Igor Melnyk, et al.
ICLR 2019
Retrieving similar questions in online Q&A community sites is a difficult task because different users may formulate the same question in a variety of ways, using different vocabulary and structure. In this work, we propose a new neural network architecture to perform the task of semantically equivalent question retrieval. The proposed architecture, which we call BOW-CNN, combines a bag-ofwords (BOW) representation with a distributed vector representation created by a convolutional neural network (CNN). We perform experiments using data collected from two Stack Exchange communities. Our experimental results evidence that: (1) BOW-CNN is more effective than BOW based information retrieval methods such as TFIDF; (2) BOW-CNN is more robust than the pure CNN for long texts.
Pierre Dognin, Igor Melnyk, et al.
ICLR 2019
Mo Yu, Wenpeng Yin, et al.
ACL 2017
Ramesh Nallapati, Bowen Zhou, et al.
CoNLL 2016
Matthias Kormaksson, Luciano Barbosa, et al.
ICDM 2014