Findings of the Third Workshop on Neural Generation and Translation

By Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew M. Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh. 2019

31 citations recorded by the source corpus.

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 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “Randomly perturbing game score values without invalidating overall game outcome is one DA strategy explored in game summary generation (Hayashi et al., 2019).”
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022, background): “…models (e.g., WikiCatSum (Perez-Beltrachini et al., 2019) and XLSum (Hasan et al., 2021)), and a class of data-to-text datasets with the communicative goal to generate game summaries from large sports statistic tables (e.g., Hayashi et al., 2019; Thomson et al., 2020; Puduppully et al., 2019a).”
Findings of the Fourth Workshop on Neural Generation and Translation (2020, methodology): “…that the use of both document-level models and in-domain data helps achieve better BLEU score, which has also been shown from the last work-shop (Hayashi et al., 2019). language-learning platform, 7 we grade translation-based challenges with sets of human-curated acceptable translation options.”
TaTa: A Multilingual Table-to-Text Dataset for African Languages (2022, background): “…et al., 2020), dependency parsing (De Marneffe et al., 2021), named 4 An exception is the English–German RotoWire subset created for a shared task (Hayashi et al., 2019). entity recognition (Adelani et al., 2021), summarization (Varab and Schluter, 2021), and sentiment analysis (Muhammad et al.,…”
Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation (2019, methodology): “Following the shared task guidelines (Hayashi et al., 2019), we evaluate our models using an information extraction (IE) system.”