# Bootstrapping Generators from Noisy Data

arXiv: https://arxiv.org/abs/1804.06385 · HTML: https://mlsanity.com/paper/1804.06385/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [Controlling Hallucinations at Word Level in Data-to-Text Generation](https://mlsanity.com/paper/2102.02810/) (2021)
- [Data-to-text Generation with Macro Planning](https://mlsanity.com/paper/2102.02723/) (2021)
- [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022)
- [Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View](https://mlsanity.com/paper/2102.08585/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [A Survey of Automatic Hallucination Evaluation on Natural Language Generation](https://mlsanity.com/paper/2404.12041/) (2024)
- [Improving User Controlled Table-To-Text Generation Robustness](https://mlsanity.com/paper/2302.09820/) (2023)
- [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://mlsanity.com/paper/2311.09467/) (2023)
- [A Systematic Review of Data-to-Text NLG](https://mlsanity.com/paper/2402.08496/) (2024)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [DISK: Domain-constrained Instance Sketch for Math Word Problem Generation](https://mlsanity.com/paper/2204.04686/) (2022)

## Abstract (arXiv)

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.