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Graph-to-Sequence

10 papers with code ยท Natural Language Processing

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Enhancing AMR-to-Text Generation with Dual Graph Representations

IJCNLP 2019

Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges.

GRAPH-TO-SEQUENCE TEXT GENERATION

Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model

19 Oct 2019

Natural question generation (QG) aims to generate questions from a passage and an answer.

GRAPH-TO-SEQUENCE QUESTION GENERATION

DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums

22 Aug 2019

In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.

GRAPH-TO-SEQUENCE

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

TACL 2019

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation.

GRAPH-TO-SEQUENCE MACHINE TRANSLATION TEXT GENERATION

Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation

14 Aug 2019

Natural question generation (QG) is a challenging yet rewarding task, that aims to generate questions given an input passage and a target answer.

GRAPH-TO-SEQUENCE QUESTION GENERATION

Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

ACL 2019

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.

GRAPH-TO-SEQUENCE

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

4 Jun 2019

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.

GRAPH-TO-SEQUENCE

Structural Neural Encoders for AMR-to-text Generation

NAACL 2019

AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs.

GRAPH-TO-SEQUENCE TEXT GENERATION

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

24 May 2019

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.

GRAPH-TO-SEQUENCE