15 citations · 25 across the 14 of their papers we have counts for
5 papers · 1 filter
Investigating Pretrained Language Models for Graph-to-Text Generation
Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze +1
Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recently proposed pretrained language models (PLMs) and analyze the…
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter +2
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all…
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Anne Lauscher, Olga Majewska, Leonardo F. R. Ribeiro +3
Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowle…
Metaphoric Paraphrase Generation
Kevin Stowe, Leonardo Ribeiro, Iryna Gurevych
This work describes the task of metaphoric paraphrase generation, in which we are given a literal sentence and are charged with generating a metaphoric paraphrase. We propose two d…
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Leonardo F. R. Ribeiro, Yue Zhang, Claire Gardent +1
Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communi…