# Machine Translation for Machines: the Sentiment Classification Use Case

arXiv: https://arxiv.org/abs/1910.00478 · HTML: https://mlsanity.com/paper/1910.00478/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

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

- [T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification](https://mlsanity.com/paper/2306.04996/) (2023)
- [Generate and Revise: Reinforcement Learning in Neural Poetry](https://mlsanity.com/paper/2102.04114/) (2021)
- [Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs](https://mlsanity.com/paper/2605.01224/) (2026)

## Abstract (arXiv)

We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency ("human-oriented" quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a "machine-oriented" criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with machine-oriented translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.