Towards Verified Robustness under Text Deletion Interventions
Neural networks are widely used in Natural Language Processing, yet despite their empirical successes, their behaviour is brittle: they are both over-sensitive to small input changes, and under-sensitive to deletions of large fractions of input text. This paper aims to tackle under-sensitivity in the context of natural language inference by ensuring that models do not become more confident in their predictions as arbitrary subsets of words from the input text are deleted. We develop a novel technique for formal verification of this specification for models based on the popular decomposable attention mechanism by employing the efficient yet effective interval bound propagation (IBP) approach. Using this method we can efficiently prove, given a model, whether a particular sample is free from the under-sensitivity problem. We compare different training methods to address under-sensitivity, and compare metrics to measure it. In our experiments on the SNLI and MNLI datasets, we observe that IBP training leads to a significantly improved verified accuracy. On the SNLI test set, we can verify 18.4% of samples, a substantial improvement over only 2.8% using standard training.
This work is an application of interval bound propagation on evaluating the robustness of NLI model. This work is well-motivated, assuming that the confidence of a neural model should be lower when part of the sentence is missed. However, the application of vanilla IBP is quite limited in certain model architectures. In this work, the author considers specifically the decomposable attention model, which is a very shallow network, and not a state-of-the-art model anymore. It is non-trivial to adapt the proposed method to other more advanced models, such as the ones based on the Transformer model. Hence, this work does not make enough contribution to be accepted.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards Verified Robustness under Text Deletion Interventions
Neural networks are widely used in Natural Language Processing, yet despite their empirical successes, their behaviour is brittle: they are both over-sensitive to small input changes, and under-sensitive to deletions of large fractions of input text. This paper aims to tackle under-sensitivity in the context of natural language inference by ensuring that models do not become more confident in their predictions as arbitrary subsets of words from the input text are deleted. We develop a novel technique for formal verification of this specification for models based on the popular decomposable attention mechanism by employing the efficient yet effective interval bound propagation (IBP) approach. Using this method we can efficiently prove, given a model, whether a particular sample is free from the under-sensitivity problem. We compare different training methods to address under-sensitivity, and compare metrics to measure it. In our experiments on the SNLI and MNLI datasets, we observe that IBP training leads to a significantly improved verified accuracy. On the SNLI test set, we can verify 18.4% of samples, a substantial improvement over only 2.8% using standard training.
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 work is an application of interval bound propagation on evaluating the robustness of NLI model. This work is well-motivated, assuming that the confidence of a neural model should be lower when part of the sentence is missed. However, the application of vanilla IBP is quite limited in certain model architectures. In this work, the author considers specifically the decomposable attention model, which is a very shallow network, and not a state-of-the-art model anymore. It is non-trivial to adapt the proposed method to other more advanced models, such as the ones based on the Transformer model. Hence, this work does not make enough contribution to be accepted.
assistant
This work is an application of interval bound propagation on evaluating the robustness of NLI model. This work is well-motivated, assuming that the confidence of a neural model should be lower when part of the sentence is missed. However, the application of vanilla IBP is quite limited in certain model architectures. In this work, the author considers specifically the decomposable attention model, which is a very shallow network, and not a state-of-the-art model anymore. It is non-trivial to adapt the proposed method to other more advanced models, such as the ones based on the Transformer model. Hence, this work does not make enough contribution to be accepted.