# Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation

arXiv: https://arxiv.org/abs/2306.11900 · HTML: https://mlsanity.com/paper/2306.11900/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study](https://mlsanity.com/paper/2106.10719/) (2021)

## Built on by (most cited first, 6 of 6)

- [CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding](https://mlsanity.com/paper/2503.10688/) (2025)
- [Benchmarking Machine Translation on Chinese Social Media Texts](https://mlsanity.com/paper/2601.22931/) (2026)
- [A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content](https://mlsanity.com/paper/2410.03277/) (2024)
- [Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems](https://mlsanity.com/paper/2503.16158/) (2025)
- [Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?](https://mlsanity.com/paper/2410.06338/) (2024)
- [Beyond Semantics: Measuring Fine-Grained Emotion Preservation in Small Language Model-Based Machine Translation](https://mlsanity.com/paper/2604.27920/) (2026)

## Abstract (arXiv)

In this paper, we focus on how current Machine Translation (MT) tools perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a framework proposed in this paper. We propose this evaluation framework based on the Multidimensional Quality Metrics (MQM) and perform a detailed error analysis of the MT outputs. From our analysis, we observe that about 50% of the MT outputs fail to preserve the original emotion. After further analysis of the errors, we find that emotion carrying words and linguistic phenomena such as polysemous words, negation, abbreviation etc., are common causes for these translation errors.