Petabyte

Original paper:  https://www.aclweb.org/anthology/N19-1318 Here is a quick summary: The research purpose is to identify helpful posts from discussion threads in forums, especially long-running discussions. The approach is to model the relevance of each post concerning the original post and the novelty (not presented in the earlier posts of the discussion thread)of a  post based on a windowed context. To model, the 'relevance' the original post and the target post are encoded using an RNN (GRU). The encoded sequences are then element-wise multiplied. As for modeling of the 'novelty,' the target post and the past K posts (where K is the number of past posts taken into context. A 'K' between 11 to 7 worked best for the Reddit dataset used in the experiment - performance stops improving after a certain number of posts taken into context) are also encoded using the same RNN text encoder. Once the 'K' posts are text encoded it is then fed thru another R

Original paper -  https://www.aclweb.org/anthology/N19-1221 Here is a quick summary: In the paper submitted by Facebook AI, London to the recent NAACL (North American Chapter of the Association for Computational Linguistics) conference held in Minneapolis, they presented a novel approach using Graph Convolutional Networks to outperform some the best ways to detect abusive language on the internet. The approach made use of a heterogeneous graph that contains an authors community network and tweets. The graph is then used to predict the class and generate an embedding. In the paper's experiments, the researchers used embeddings from node2vec (sample implementation here https://snap.stanford.edu/node2vec/) and a 2-layer Graph Convolutional Network.  The Graph Convolutional Network that represents the author's profiles and tweets were used to predict the author's tweet into three classes using a softmax layer as the output layer of the network. To extract the embedding f