# Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research

arXiv: https://arxiv.org/abs/2104.07874 · HTML: https://mlsanity.com/paper/2104.07874/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 94 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [HAT: Hardware-Aware Transformers for Efficient Natural Language Processing](https://mlsanity.com/paper/2005.14187/) (2020)
- [Investigating African-American Vernacular English in Transformer-Based Text Generation](https://mlsanity.com/paper/2010.02510/) (2020)
- [Jointly Embedding Entities and Text with Distant Supervision](https://mlsanity.com/paper/1807.03399/) (2018)
- [Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility](https://mlsanity.com/paper/1806.02814/) (2018)
- [Lightweight Convolutional Representations for On-Device Natural Language Processing](https://mlsanity.com/paper/2002.01535/) (2020)
- [Costs to Consider in Adopting NLP for Your Business](https://mlsanity.com/paper/2012.08958/) (2020)
- [HARE: a Flexible Highlighting Annotator for Ranking and Exploration](https://mlsanity.com/paper/1908.11302/) (2019)
- [Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health](https://mlsanity.com/paper/2011.13978/) (2020)

## Built on by (most cited first, 7 of 7)

- [Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and Beyond](https://mlsanity.com/paper/2201.11443/) (2022)
- [Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks](https://mlsanity.com/paper/2111.01340/) (2021)
- [Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges](https://mlsanity.com/paper/2405.10630/) (2024)
- [AI Thinking: A framework for rethinking artificial intelligence in practice](https://mlsanity.com/paper/2409.12922/) (2024)
- [Benchmarking Machine Translation on Chinese Social Media Texts](https://mlsanity.com/paper/2601.22931/) (2026)
- [Beyond Text: Characterizing Domain Expert Needs in Document Research](https://mlsanity.com/paper/2504.12495/) (2025)
- [Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia](https://mlsanity.com/paper/2311.13755/) (2023)

## Abstract (arXiv)

Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.