# Lutma: a Frame-Making Tool for Collaborative FrameNet Development

arXiv: https://arxiv.org/abs/2205.11840 · HTML: https://mlsanity.com/paper/2205.11840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [Changing the World by Changing the Data](https://mlsanity.com/paper/2105.13947/) (2021)
- [Low Resource Neural Machine Translation: A Benchmark for Five African Languages](https://mlsanity.com/paper/2003.14402/) (2020)
- [Establishing Baselines for Text Classification in Low-Resource Languages](https://mlsanity.com/paper/2005.02068/) (2020)

## Abstract (arXiv)

This paper presents Lutma, a collaborative, semi-constrained, tutorial-based tool for contributing frames and lexical units to the Global FrameNet initiative. The tool parameterizes the process of frame creation, avoiding consistency violations and promoting the integration of frames contributed by the community with existing frames. Lutma is structured in a wizard-like fashion so as to provide users with text and video tutorials relevant for each step in the frame creation process. We argue that this tool will allow for a sensible expansion of FrameNet coverage in terms of both languages and cultural perspectives encoded by them, positioning frames as a viable alternative for representing perspective in language models.