NuLG

NuLG is a modular, open-source framework for natural language generation and related tasks. It provides tools for building, training, evaluating, and deploying language generation systems, with emphasis on accessibility and interoperability across research and production environments. The project supports a range of generation tasks including dialogue, summarization, translation post-editing, and data-to-text, and it is designed to be language-agnostic with multilingual capabilities.

NuLG originated as a collaborative effort among researchers and developers seeking a cohesive toolchain for experimentation with transformer-based models. It is community-governed and hosted on major code repositories, inviting contributions from academia and industry. The architecture is modular, comprising a core runtime, a model zoo of pre-trained weights, data processing and augmentation utilities, training pipelines with experiment tracking, an evaluation suite with standard metrics, and deployment components for serving at scale.

Key features include support for fine-tuning and adapter-based transfer learning, configurable generation settings, safety and alignment controls, provenance and logging for reproducibility, and an extensible plugin system. The framework integrates with popular ecosystems and can be used with or without cloud resources, enabling local development and large-scale training.

NuLG is used in research to prototype controllable generation, evaluate multilingual models, and compare approaches to data-to-text tasks. In practice, it faces challenges common to NLP tooling, such as data quality, bias, and the need for robust evaluation.

See also: natural language processing, language model, transformers, text generation, machine learning.