Papers Graph-to-Sequence
“Graph-to-Sequence” 태그가 달린 논문 48편 · 필터 해제
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
Molecular property prediction uses molecular structure to infer chemical properties. Chemically interpretable representations that capture meaningful intramolecular interactions enhance the usability and effectiveness of…
Graph-to-SequenceMolecular Property Predictionmolecular representationProperty Prediction+2PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
Traffic flow prediction is the foundation of intelligent traffic management systems. Current methods prioritize the development of intricate models to capture spatio-temporal correlations, yet they often neglect the expl…
feature selectionGraph-to-SequenceTraffic PredictionEzSQL: An SQL intermediate representation for improving SQL-to-text Generation
The SQL-to-text generation task traditionally uses template base, Seq2Seq, tree-to-sequence, and graph-to-sequence models. Recent models take advantage of pre-trained generative language models for this task in the Seq2S…
Graph-to-SequenceLanguage ModelingLanguage ModellingSQL-to-Text+3Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model
Knowledge Graph-to-Text (G2T) generation involves verbalizing structured knowledge graphs into natural language text. Recent advancements in Pretrained Language Models (PLMs) have improved G2T performance, but their effe…
Data-to-Text GenerationGraph-to-SequenceKnowledge GraphsLanguage Modeling+3Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
The visual question generation (VQG) task aims to generate human-like questions from an image and potentially other side information (e.g. answer type). Previous works on VQG fall in two aspects: i) They suffer from one …
Graph-to-SequenceImplicit RelationsQuestion GenerationQuestion-GenerationUAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment
Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recen…
Graph-to-Sequencemolecular representationRepresentation LearningRetrosynthesis+1Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project in Santander, Spain, which has relied on …
Graph-to-SequenceA 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. Although RNN-based neural models show promising results by jointly l…
Decoderglobal-optimizationGraph-to-SequenceIntent Detection+4GraphMR: Graph Neural Network for Mathematical Reasoning
Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or rela…
Graph Neural NetworkGraph-to-SequenceMathematical ReasoningTopic-Guided Abstractive Multi-Document Summarization
A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneo…
Document SummarizationGraph-to-SequenceMulti-Document SummarizationMulti-Task LearningPermutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each …
Data AugmentationGraph-to-SequenceMachine TranslationRetrosynthesis+3AMR-to-text Generation with Graph Structure Reconstruction and Coverage
Generating text from semantic representations such as AMR is a challenging task. Previous research formalizes this task as a graph-to-sequence learning problem and uses various graph neural networks to model the graph st…
AMR-to-Text GenerationGraph-to-SequenceText GenerationTranslationProving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models
We target the problem of provably computing the equivalence between two complex expression trees. To this end, we formalize the problem of equivalence between two such programs as finding a set of semantics-preserving re…
Graph-to-SequencevalidLearning 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.…
Graph-to-SequenceAsk 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). Despite promising results have been achieve…
Graph-to-SequenceQuestion GenerationQuestion-GenerationGraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis
This paper introduces a graphical representation approach of prosody boundary (GraphPB) in the task of Chinese speech synthesis, intending to parse the semantic and syntactic relationship of input sequences in a graphica…
DecoderGraph EmbeddingGraph Neural NetworkGraph-to-Sequence+4Graph-to-Sequence Neural Machine Translation
Neural machine translation (NMT) usually works in a seq2seq learning way by viewing either source or target sentence as a linear sequence of words, which can be regarded as a special case of graph, taking words in the se…
Graph-to-SequenceMachine TranslationNMTSentence+1Sparse 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. The challenge here proliferate over the standard vision conditioned sentence-level generation (e.g., im…
DecoderGraph-to-SequenceSentenceVideo CaptioningLine Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation {--} A graph-to-sequence task aiming to recover nat…
AMR-to-Text GenerationGraph AttentionGraph-to-SequenceText GenerationHeterogeneous Graph Transformer for Graph-to-Sequence Learning
The graph-to-sequence (Graph2Seq) learning aims to transduce graph-structured representations to word sequences for text generation. Recent studies propose various models to encode graph structure. However, most previous…
AMR-to-Text GenerationGraph-to-SequenceMachine TranslationText Generation+1