# An Operation Sequence Model for Explainable Neural Machine Translation

arXiv: https://arxiv.org/abs/1808.09688 · HTML: https://mlsanity.com/paper/1808.09688/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

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

- [Local Interpretations for Explainable Natural Language Processing: A Survey](https://mlsanity.com/paper/2103.11072/) (2021)
- [Towards Explainable Evaluation Metrics for Machine Translation](https://mlsanity.com/paper/2306.13041/) (2023)
- [Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments](https://mlsanity.com/paper/2307.03354/) (2023)
- [Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation](https://mlsanity.com/paper/2211.05967/) (2022)
- [A Discussion on Building Practical NLP Leaderboards: The Case of Machine Translation](https://mlsanity.com/paper/2106.06292/) (2021)
- [An empirical analysis of phrase-based and neural machine translation](https://mlsanity.com/paper/2103.03108/) (2021)

## Abstract (arXiv)

We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.