# We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processing

arXiv: https://arxiv.org/abs/2110.05464 · HTML: https://mlsanity.com/paper/2110.05464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study](https://mlsanity.com/paper/1808.05697/) (2018)
- [ALICE: Active Learning with Contrastive Natural Language Explanations](https://mlsanity.com/paper/2009.10259/) (2020)
- [FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity](https://mlsanity.com/paper/1808.07261/) (2018)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [Data Readiness Levels](https://mlsanity.com/paper/1705.02245/) (2017)
- [Best Practices for Managing Data Annotation Projects](https://mlsanity.com/paper/2009.11654/) (2020)

## Abstract (arXiv)

In this paper, we identify the state of data as being an important reason for failure in applied Natural Language Processing (NLP) projects. We argue that there is a gap between academic research in NLP and its application to problems outside academia, and that this gap is rooted in poor mutual understanding between academic researchers and their non-academic peers who seek to apply research results to their operations. To foster transfer of research results from academia to non-academic settings, and the corresponding influx of requirements back to academia, we propose a method for improving the communication between researchers and external stakeholders regarding the accessibility, validity, and utility of data based on Data Readiness Levels citelawrence2017data. While still in its infancy, the method has been iterated on and applied in multiple innovation and research projects carried out with stakeholders in both the private and public sectors. Finally, we invite researchers and practitioners to share their experiences, and thus contributing to a body of work aimed at raising awareness of the importance of data readiness for NLP.