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KB-to-Language Generation | State-of-the-Art

table-to-text-generation

KB-to-Language Generation

3260 papers • 126 benchmarks • 313 datasets

Given information from a knowledge base, generate a description of this information in natural language.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in table-to-text-generation

Trend

Dataset

Best Model

Actions

Wikipedia Person and Animal Dataset

Libraries

Use these libraries to find table-to-text-generation models and implementations

Datasets

Wikipedia Person and Animal Dataset

Subtasks

No subtasks available.

Most implemented papers

Describing a Knowledge Base

Qingyun Wang, Lifu Huang, Boliang Zhang, Heng Ji, Kevin Knight, Xiaoman Pan•Wed Sep 05 2018

This work builds a generation framework based on a pointer network which can copy facts from the input KB, and adds two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to captured the inter-dependencies among related slots.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

Investigating Pretrained Language Models for Graph-to-Text Generation

Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, Iryna Gurevych•Wed Jul 15 2020

It is suggested that the PLMs benefit from similar facts seen during pretraining or fine-tuning, such that they perform well even when the input graph is reduced to a simple bag of node and edge labels.

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Paper Graph

Stage-wise Fine-tuning for Graph-to-Text Generation

Nazneen Rajani, Semih Yavuz, Qingyun Wang, Heng Ji, Victoria Lin•Sun May 16 2021

This paper proposes a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes model on Wikipedia before adapting to the graph- to-text generation, and proposes a novel tree-level embedding method to capture the inter-dependency structures of the input graph.

26 0

Paper Graph

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