# Shallow Domain Adaptive Embeddings for Sentiment Analysis

arXiv: https://arxiv.org/abs/1908.06082 · HTML: https://mlsanity.com/paper/1908.06082/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [DISCO: Comprehensive and Explainable Disinformation Detection](https://mlsanity.com/paper/2203.04928/) (2022)
- [Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation](https://mlsanity.com/paper/2107.01598/) (2021)
- [Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?](https://mlsanity.com/paper/2203.16926/) (2022)

## Abstract (arXiv)

This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics. We propose a domain adaptation layer learns weights to combine a generic and a domain specific (DS) word embedding into a domain adapted (DA) embedding. The DA word embeddings are then used as inputs to a generic encoder + classifier framework to perform a downstream task such as classification. This adaptation layer is particularly suited to datasets that are modest in size, and which are, therefore, not ideal candidates for (re)training a deep neural network architecture. Results on binary and multi-class classification tasks using popular encoder architectures, including current state-of-the-art methods (with and without the shallow adaptation layer) show the effectiveness of the proposed approach.