Graph-to-Sequence
26 papers with code • 2 benchmarks • 3 datasets
Mapping an input graph to a sequence of vectors.
Libraries
Use these libraries to find Graph-to-Sequence models and implementations
WebNLG
EventNarrative
ENT-DESC
Latest papers with no code
A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems
Effectively decoding semantic frames in task-oriented dialogue systems remains a challenge, which typically includes intent detection and slot filling.
Topic-Guided Abstractive Multi-Document Summarization
A critical point of multi-document summarization (MDS) is to learn the relations among various documents.
AMR-to-text Generation with Graph Structure Reconstruction and Coverage
To consider the coverage of AMR graphs, we design a coverage mechanism to solve the problem of information under-translation or over-translation in AMR-to-text generation.
Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models
We target the problem of provably computing the equivalence between two complex expression trees.
Learning Axioms to Compute Verifiable Symbolic Expression Equivalence Proofs Using Graph-to-Sequence Networks
We target the problem of proving the semantic equivalence between two complex expressions represented as typed trees, and demonstrate our system on expressions from a rich multi-type symbolic language for linear algebra.
Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e. g. answer type or the answer itself).
GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis
Graph-to-sequence model is proposed and formed by a graph encoder and an attentional decoder.
Graph-to-Sequence Neural Machine Translation
In the light of the current NMT models more or less capture graph information among the sequence in a latent way, we present a graph-to-sequence model facilitating explicit graph information capturing.
Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation
Generating longer textual sequences when conditioned on the visual information is an interesting problem to explore.
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
We also adopt graph attention networks with higher-order neighborhood information to encode the rich structure in AMR graphs.
