# A Data-Centric Framework for Composable NLP Workflows

arXiv: https://arxiv.org/abs/2103.01834 · HTML: https://mlsanity.com/paper/2103.01834/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation](https://mlsanity.com/paper/1903.10122/) (2019)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [Generalizing Natural Language Analysis through Span-relation Representations](https://mlsanity.com/paper/1911.03822/) (2019)
- [YEDDA: A Lightweight Collaborative Text Span Annotation Tool](https://mlsanity.com/paper/1711.03759/) (2017)
- [Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation](https://mlsanity.com/paper/1809.00794/) (2018)

## Built on by (most cited first, 5 of 5)

- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [DataLab: A Platform for Data Analysis and Intervention](https://mlsanity.com/paper/2202.12875/) (2022)
- [Cross-document Event Identity via Dense Annotation](https://mlsanity.com/paper/2109.06417/) (2021)
- [A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets](https://mlsanity.com/paper/2304.00483/) (2023)
- [DataCI: A Platform for Data-Centric AI on Streaming Data](https://mlsanity.com/paper/2306.15538/) (2023)

## Abstract (arXiv)

Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte (for workflow infrastructure and NLP function processors) and Stave (for user interaction, visualization, and annotation).