Improving and Stabilizing Deep Energy-Based Learning
Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger & McCallum, 2016). Tu & Gimpel (2018) developed an efficient framework for energy-based models by training “inference networks” to approximate structured inference instead of using gradient descent. However, their alternating optimization approach suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning. In this paper, we contribute several strategies to stabilize and improve this joint training of energy functions and inference networks for structured prediction. We design a compound objective to jointly train both cost-augmented and test-time inference networks along with the energy function. We propose joint parameterizations for the inference networks that encourage them to capture complementary functionality during learning. We empirically validate our strategies on two sequence labeling tasks, showing easier paths to strong performance than prior work, as well as further improvements with global energy terms.
This submission is a follow-up on the SPEN model originally proposed in Tu & Gimpel (2018), which trains an energy-based model for structural prediction. I am voting to reject this work for the lack of contributions and significance. While the overall writing is clear and well-motivated, basically the main contributions boil down to a set of minor modifications to model architecture (separating the inference network) and hacking the original objectives (Sec 4&5). On the significance side, the improvements seem incremental. Since this paper focuses on empirical efforts, stronger results are needed to make the case. Minors: It*s not clear what is the motivation behind the design of the global energy term in Eqn (6). And the author(s) should try to avoid citing papers in the abstract.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Improving and Stabilizing Deep Energy-Based Learning
Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger & McCallum, 2016). Tu & Gimpel (2018) developed an efficient framework for energy-based models by training “inference networks” to approximate structured inference instead of using gradient descent. However, their alternating optimization approach suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning. In this paper, we contribute several strategies to stabilize and improve this joint training of energy functions and inference networks for structured prediction. We design a compound objective to jointly train both cost-augmented and test-time inference networks along with the energy function. We propose joint parameterizations for the inference networks that encourage them to capture complementary functionality during learning. We empirically validate our strategies on two sequence labeling tasks, showing easier paths to strong performance than prior work, as well as further improvements with global energy terms.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This submission is a follow-up on the SPEN model originally proposed in Tu & Gimpel (2018), which trains an energy-based model for structural prediction. I am voting to reject this work for the lack of contributions and significance. While the overall writing is clear and well-motivated, basically the main contributions boil down to a set of minor modifications to model architecture (separating the inference network) and hacking the original objectives (Sec 4&5). On the significance side, the improvements seem incremental. Since this paper focuses on empirical efforts, stronger results are needed to make the case. Minors: It*s not clear what is the motivation behind the design of the global energy term in Eqn (6). And the author(s) should try to avoid citing papers in the abstract.
assistant
This submission is a follow-up on the SPEN model originally proposed in Tu & Gimpel (2018), which trains an energy-based model for structural prediction. I am voting to reject this work for the lack of contributions and significance. While the overall writing is clear and well-motivated, basically the main contributions boil down to a set of minor modifications to model architecture (separating the inference network) and hacking the original objectives (Sec 4&5). On the significance side, the improvements seem incremental. Since this paper focuses on empirical efforts, stronger results are needed to make the case. Minors: It*s not clear what is the motivation behind the design of the global energy term in Eqn (6). And the author(s) should try to avoid citing papers in the abstract.