# Few-shot learning approaches for classifying low resource domain specific software requirements

arXiv: https://arxiv.org/abs/2302.06951 · HTML: https://mlsanity.com/paper/2302.06951/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## 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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition](https://mlsanity.com/paper/2112.00283/) (2021)
- [Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE](https://mlsanity.com/paper/2211.09084/) (2022)

## Abstract (arXiv)

With the advent of strong pre-trained natural language processing models like BERT, DeBERTa, MiniLM, T5, the data requirement for industries to fine-tune these models to their niche use cases has drastically reduced (typically to a few hundred annotated samples for achieving a reasonable performance). However, the availability of even a few hundred annotated samples may not always be guaranteed in low resource domains like automotive, which often limits the usage of such deep learning models in an industrial setting. In this paper we aim to address the challenge of fine-tuning such pre-trained models with only a few annotated samples, also known as Few-shot learning. Our experiments focus on evaluating the performance of a diverse set of algorithms and methodologies to achieve the task of classifying BOSCH automotive domain textual software requirements into 3 categories, while utilizing only 15 annotated samples per category for fine-tuning. We find that while SciBERT and DeBERTa based models tend to be the most accurate at 15 training samples, their performance improvement scales minimally as the number of annotated samples is increased to 50 in comparison to Siamese and T5 based models.