AMR-ден мәтін генерациясы
Related Papers
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
AbstractRecent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are …...
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
AbstractAMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph representations. Previously, Graph Convolution Networks …...
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
AbstractEfficient 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 …...
Online Back-Parsing for AMR-to-Text Generation
AbstractAMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders …...
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
AbstractDue to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and …...
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning
AbstractAbstract Meaning Representation (AMR) offers a unified semantic representation for natural language sentences. Thus transformation between AMR and text yields two transition tasks in opposite directions, i.e., Text-to-AMR parsing and …...