# Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models

arXiv: https://arxiv.org/abs/1910.00275 · HTML: https://mlsanity.com/paper/1910.00275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Using dependency parsing for few-shot learning in distributional semantics](https://mlsanity.com/paper/2205.06168/) (2022)

## Abstract (arXiv)

Word embeddings are an essential component in a wide range of natural language processing applications. However, distributional semantic models are known to struggle when only a small number of context sentences are available. Several methods have been proposed to obtain higher-quality vectors for these words, leveraging both this context information and sometimes the word forms themselves through a hybrid approach. We show that the current tasks do not suffice to evaluate models that use word-form information, as such models can easily leverage word forms in the training data that are related to word forms in the test data. We introduce 3 new tasks, allowing for a more balanced comparison between models. Furthermore, we show that hyperparameters that have largely been ignored in previous work can consistently improve the performance of both baseline and advanced models, achieving a new state of the art on 4 out of 6 tasks.