SimpleNets: Quality Estimation with Resource-Light Neural Networks

By Gustavo Paetzold, Lucia Specia. 2016

6 citations recorded by the source corpus.

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Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ondrej Bojar
2. Philipp Koehn
3. Lucia Specia
4. Graham Neubig
5. Barry Haddow
6. Matt Post
7. Rico Sennrich
8. Felix Stahlberg

In Natural Language Processing

1. Ondrej Bojar
2. Philipp Koehn
3. Lucia Specia
4. Barry Haddow
5. Graham Neubig
6. Christof Monz
7. Yvette Graham
8. Matt Post

In Evaluation & Benchmarks

1. Ondrej Bojar
2. Lucia Specia
3. Philipp Koehn
4. Yvette Graham
5. Matt Post
6. Barry Haddow
7. Christian Federmann
8. Marco Turchi

Citations in the corpus

• Findings of the 2016 Conference on Machine Translation (2016, background): “CDACM Centre for Development of Advanced Computing, India (Patel and M, 2016) POSTECH Pohang University of Science and Technology, Republic of Korea (Kim and Lee, 2016) RTM Referential Translation Machines, Turkey (Bicici, 2016b) SHEF University of Sheffield, UK (Paetzold and Specia, 2016) SHEF-LIUM”
• Intrinsic Evaluation of Summarization Datasets (2020, background): “Quality estimation, often centered on natural language generation, is the task of measuring system-generated output quality (Paetzold and Specia, 2016; Yuan and Sharoff, 2020).”