# Learning Word Embeddings with Domain Awareness

arXiv: https://arxiv.org/abs/1906.03249 · HTML: https://mlsanity.com/paper/1906.03249/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

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

- [Mere Contrastive Learning for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2208.08678/) (2022)

## Abstract (arXiv)

Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory performances when training data originate from heterogeneous domains. In this paper, we propose two novel mechanisms for domain-aware word embedding training, namely domain indicator and domain attention, which integrate domain-specific knowledge into the widely used SG and CBOW models, respectively. The two methods are based on a joint learning paradigm and ensure that words in a target domain are intensively focused when trained on a source domain corpus. Qualitative and quantitative evaluation confirm the validity and effectiveness of our models. Compared to baseline methods, our method is particularly effective in near-cold-start scenarios.