# Mapping Unseen Words to Task-Trained Embedding Spaces

arXiv: https://arxiv.org/abs/1510.02387 · HTML: https://mlsanity.com/paper/1510.02387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2015.

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

- [Integrating Approaches to Word Representation](https://mlsanity.com/paper/2109.04876/) (2021)

## Abstract (arXiv)

We consider the supervised training setting in which we learn task-specific word embeddings. We assume that we start with initial embeddings learned from unlabelled data and update them to learn task-specific embeddings for words in the supervised training data. However, for new words in the test set, we must use either their initial embeddings or a single unknown embedding, which often leads to errors. We address this by learning a neural network to map from initial embeddings to the task-specific embedding space, via a multi-loss objective function. The technique is general, but here we demonstrate its use for improved dependency parsing (especially for sentences with out-of-vocabulary words), as well as for downstream improvements on sentiment analysis.