# Findings of the Third Workshop on Neural Generation and Translation

arXiv: https://arxiv.org/abs/1910.13299 · HTML: https://mlsanity.com/paper/1910.13299/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [GEMv2: Multilingual NLG Benchmarking in a Single Line of Code](https://mlsanity.com/paper/2206.11249/) (2022)
- [Data-to-text Generation with Variational Sequential Planning](https://mlsanity.com/paper/2202.13756/) (2022)
- [TaTa: A Multilingual Table-to-Text Dataset for African Languages](https://mlsanity.com/paper/2211.00142/) (2022)
- [Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation](https://mlsanity.com/paper/2307.09701/) (2023)
- [Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation](https://mlsanity.com/paper/2105.03432/) (2021)
- [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 Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.