# Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

## TL;DR

This paper introduces PARENT, a new evaluation metric for table-to-text generation that better aligns with human judgments by considering the semi-structured data, addressing issues caused by divergent reference texts in datasets.

## Contribution

The paper proposes PARENT, an evaluation metric that improves correlation with human judgments by aligning references with data, and demonstrates its effectiveness on multiple datasets.

## Key findings

- PARENT outperforms BLEU and ROUGE in correlating with human judgments.
- PARENT is easier to use than existing data-driven evaluation methods.
- PARENT is applicable to both WikiBio and WebNLG datasets.

## Abstract

Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge. We propose a new metric, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall. Through a large scale human evaluation study of table-to-text models for WikiBio, we show that PARENT correlates with human judgments better than existing text generation metrics. We also adapt and evaluate the information extraction based evaluation proposed by Wiseman et al (2017), and show that PARENT has comparable correlation to it, while being easier to use. We show that PARENT is also applicable when the reference texts are elicited from humans using the data from the WebNLG challenge.