Findings of the Fourth Workshop on Neural Generation and Translation

By Kenneth Heafield, Hiroaki Hayashi, Yusuke Oda, Ioannis Konstas, Andrew M. Finch, Graham Neubig, Xian Li, Alexandra Birch. 2020

16 citations recorded by the source corpus.

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three 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 and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.

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

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Citations in the corpus

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021, background): “The shared task on document-level generation and translation featured German and English generation challenges (Heafield et al., 2020).”
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking (2021, background): “Some challenge tasks in recent years have required the reporting of prediction costs or have instituted limits on the maximum costs that can be incurred, as in the EfficientQA challenge [27, 45].”
Gender bias amplification during Speed-Quality optimization in Neural Machine Translation (2021, unknown): “Even when speed optimizations are evaluated in shared tasks, they typically use BLEU (Papineni et al., 2002; Heaﬁeld et al., 2020) to approximate quality, thereby missing gender bias.”