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--- nlp:structured_prediction_energy_networks [2021/04/01 17:19] – [Papers] jmflanig
+++ nlp:structured_prediction_energy_networks [2023/06/15 07:36] (current) – external edit 127.0.0.1
@@ Line 12: / Line 12: @@
   * [[https://arxiv.org/pdf/1703.05667.pdf|Belanger et al 2017 - End-to-End Learning for Structured Prediction Energy Networks]]
   * [[https://www.aclweb.org/anthology/N18-2021.pdf|Rooshenas et al 2018 - Training Structured Prediction Energy Networks with Indirect Supervision]]
+  * [[https://arxiv.org/pdf/1812.09603.pdf|Rooshenas et al 2018 - Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks]]
   * Kevin Gimple's papers
     * [[https://arxiv.org/pdf/1803.03376.pdf|Tu & Gimple 2018 - Learning Approximate Inference Networks for Structured Prediction]]
@@ Line 19: / Line 20: @@
   * [[https://www.aclweb.org/anthology/W19-4109.pdf|Trinh et al 2019 - Energy-Based Modelling for Dialogue State Tracking]]
   * [[https://www.aclweb.org/anthology/2020.spnlp-1.5.pdf|Trinh et al 2020 - Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking]]
-  * [[https://arxiv.org/pdf/2009.13267.pdf|Bhattacharyya et al 2020 - Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models]]
+  * [[https://arxiv.org/pdf/2009.13267.pdf|Bhattacharyya et al 2020 - Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models]] Interesting paper, but has some flaws.  First, the energy-based models (EBMs) use BERT.  For a fair comparision of the merits of EBMs, they would compare the baseline to EBMs without BERT.  Second, they use reranking and do not attempt to use the ideas of SPENs to improve the decoding.
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