# On the Weaknesses of Reinforcement Learning for Neural Machine Translation

Authors: Leshem Choshen, Lior Fox, Zohar Aizenbud, Omri Abend
Venue: 8th International Conference on Learning Representations, {ICLR} 2020, Addis Ababa, Ethiopia, April 26-30, 2020 (2020)

## Abstract
Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). However, little is known about what and how these methods learn in the context of MT. We prove that one of the most common RL methods for MT does not optimize the expected reward, as well as show that other methods take an infeasibly long time to converge. In fact, our results suggest that RL practices in MT are likely to improve performance only where the pre-trained parameters are already close to yielding the correct translation. Our findings further suggest that observed gains may be due to effects unrelated to the training signal, but rather from changes in the shape of the distribution curve.

## Links
- arXiv: https://arxiv.org/abs/1907.01752
- PDF: https://arxiv.org/pdf/1907.01752
- HTML: https://ar5iv.labs.arxiv.org/html/1907.01752
- Hugging Face: https://huggingface.co/papers/1907.01752
- alphaXiv: https://www.alphaxiv.org/abs/1907.01752
- Semantic Scholar: https://www.semanticscholar.org/paper/195791459
- Publisher: https://openreview.net/forum?id=H1eCw3EKvH

## How to cite
@inproceedings{DBLP:conf/iclr/ChoshenFAA20,
author       = {Leshem Choshen and
                  Lior Fox and
                  Zohar Aizenbud and
                  Omri Abend},
  title        = {On the Weaknesses of Reinforcement Learning for Neural Machine Translation},
  booktitle    = {8th International Conference on Learning Representations, {ICLR} 2020,
                  Addis Ababa, Ethiopia, April 26-30, 2020},
  publisher    = {OpenReview.net},
  year         = {2020},
  url          = {https://openreview.net/forum?id=H1eCw3EKvH},
  timestamp    = {Thu, 21 Jan 2021 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/iclr/ChoshenFAA20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}