English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Knowledge Graph (KG)-to-Text generation aims at generating fluentnatural-language text that accurately represents the information of a givenknowledge graph. While significant progress has been made in this task byexploiting the power of pre-trained language models (PLMs) with appropriategraph structure-aware modules, existing models still fall short of generatingfaithful text, especially when the ground-truth natural-language text containsadditional information that is not present in the graph. In this paper, wedevelop a KG-to-text generation model that can generate faithfulnatural-language text from a given graph, in the presence of noisy referencetext. Our framework incorporates two core ideas: Firstly, we utilizecontrastive learning to enhance the model's ability to differentiate betweenfaithful and hallucinated information in the text, thereby encouraging thedecoder to generate text that aligns with the input graph. Secondly, we empowerthe decoder to control the level of hallucination in the generated text byemploying a controllable text generation technique. We evaluate our model'sperformance through the standard quantitative metrics as well as aChatGPT-based quantitative and qualitative analysis. Our evaluationdemonstrates the superior performance of our model over state-of-the-artKG-to-text models on faithfulness.

Generating Faithful Text From a Knowledge Graph with Noisy Reference Text