# Zero-Shot Cross-Lingual Opinion Target Extraction

arXiv: https://arxiv.org/abs/1904.09122 · HTML: https://mlsanity.com/paper/1904.09122/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.00791/) (2022)
- [Cross-lingual Emotion Detection](https://mlsanity.com/paper/2106.06017/) (2021)
- [Survey of Aspect-based Sentiment Analysis Datasets](https://mlsanity.com/paper/2204.05232/) (2022)
- [LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation](https://mlsanity.com/paper/2508.09515/) (2025)
- [Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models](https://mlsanity.com/paper/2508.10366/) (2025)
- [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)
- [Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding](https://mlsanity.com/paper/2508.10369/) (2025)
- [MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2502.13718/) (2025)
- [Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2604.26619/) (2026)

## Abstract (arXiv)

Aspect-based sentiment analysis involves the recognition of so called opinion target expressions (OTEs). To automatically extract OTEs, supervised learning algorithms are usually employed which are trained on manually annotated corpora. The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a very narrow selection of languages and domains. In this work, we address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture for OTE extraction. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language. Depending on the source and target language pairs, we reach performances in a zero-shot regime of up to 77% of a model trained on target language data. Furthermore, we can increase this performance up to 87% of a baseline model trained on target language data by performing cross-lingual learning from multiple source languages.