# Findings of the Second Workshop on Neural Machine Translation and Generation

arXiv: https://arxiv.org/abs/1806.02940 · HTML: https://mlsanity.com/paper/1806.02940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [An Efficient Transformer Decoder with Compressed Sub-layers](https://mlsanity.com/paper/2101.00542/) (2021)
- [Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation](https://mlsanity.com/paper/2307.09701/) (2023)
- [MOROCCO: Model Resource Comparison Framework](https://mlsanity.com/paper/2104.14314/) (2021)
- [CUNI Non-Autoregressive System for the WMT 22 Efficient Translation Shared Task](https://mlsanity.com/paper/2212.00477/) (2022)

## Abstract (arXiv)

This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop's shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.