id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_3zucn2lewrgwneokzjgjfu4zkq	Suman Banerjee	Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems	2019	16	.pdf	application/pdf	8373	1259	71	Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, compute the dependency parse tree for each utterance in the conversation and use a GCN to capture Our contributions can be summarized as follows: (i) We use GCNs to incorporate structural information for encoding query, history and KB entities in goal-oriented dialogues (ii) We use a sequential attention mechanism to obtain query aware and history aware context representations (iii) GCNs in NLP : Recently, there has been an active interest in enriching existing encode-attenddecode models (Bahdanau et al., 2015) with structural information for various NLP tasks. To the best of our knowledge ours is the first work that uses GCNs to incorporate dependency structural information and the entity-entity graph structure in a single end-to-end neural model for goaloriented dialogue.	./cache/work_3zucn2lewrgwneokzjgjfu4zkq.pdf	./txt/work_3zucn2lewrgwneokzjgjfu4zkq.txt