On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models
Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance. However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output. Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general. Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs. Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.
 The authors provide a natural definition of adversarial examples for natural language transduction (meaning-preserving on source side while meaning-destroying on target side) and a human judgment task to measure it. They then investigate three different ways of generating adversarial examples and show that a metric based on character n-gram overlap (chrF) has a stronger correlation with human judgment. Finally, they show that adversarial training with the attack most consistent with the introduced meaning-preservation criteria results in improved robustness to this type of attack without degradation in the non-adversarial setting. Overall this is a strong paper. It is well structured, the problem studied is highly interesting and the proposed meaning-preserving criteria and human judgement will be useful to anyone interested in adversarial attacks for natural language. While the studied attack methods are fairly primitive, the empirical results are still interesting. Comments --------------- I wish the authors would include experiments with CharSwap where OOV is not forced as I*m not sure the assumption that OOV is more meaning-destroying in the target side is necessarily true (one could also argue that since the models are already trained with OOV words, they may be more robust to OOV words than in-vocabulary words in the wrong context). It would be nice to add correlation for each type of constraint as well to Table 2. The result would be even stronger if the experiment was replicated in the opposite direction or for another language pair as well. I don*t understand why the adversarial output in the second example in table 4 has a RDchrF of zero (the word July is completely dropped). From Table 6 it looks like random sampling is actually slightly better than adversarial training in terms of robustness to CharSwap attacks in the Transformer model. Moreover, the benefit of adversarial rather than random sampling is quite small in the LSTM model as well. This could be made more clear in the text. It would be interesting to see how adversarial training with the CharSwap method fares against the unconstrained and kNN attacks in table 6.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models
Adversarial examples have been shown to be an effective way of assessing the robustness of neural sequence-to-sequence (seq2seq) models, by applying perturbations to the input of a model leading to large degradation in performance. However, these perturbations are only indicative of a weakness in the model if they do not change the semantics of the input in a way that would change the expected output. Using the example of machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models taking meaning preservation into account and demonstrate that existing methods may not preserve meaning in general. Based on these findings, we propose new constraints for attacks on word-based MT systems and show, via human and automatic evaluation, that they produce more semantically similar adversarial inputs. Furthermore, we show that performing adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness without hurting test performance.
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>



 The authors provide a natural definition of adversarial examples for natural language transduction (meaning-preserving on source side while meaning-destroying on target side) and a human judgment task to measure it. They then investigate three different ways of generating adversarial examples and show that a metric based on character n-gram overlap (chrF) has a stronger correlation with human judgment. Finally, they show that adversarial training with the attack most consistent with the introduced meaning-preservation criteria results in improved robustness to this type of attack without degradation in the non-adversarial setting. Overall this is a strong paper. It is well structured, the problem studied is highly interesting and the proposed meaning-preserving criteria and human judgement will be useful to anyone interested in adversarial attacks for natural language. While the studied attack methods are fairly primitive, the empirical results are still interesting. Comments --------------- I wish the authors would include experiments with CharSwap where OOV is not forced as I*m not sure the assumption that OOV is more meaning-destroying in the target side is necessarily true (one could also argue that since the models are already trained with OOV words, they may be more robust to OOV words than in-vocabulary words in the wrong context). It would be nice to add correlation for each type of constraint as well to Table 2. The result would be even stronger if the experiment was replicated in the opposite direction or for another language pair as well. I don*t understand why the adversarial output in the second example in table 4 has a RDchrF of zero (the word July is completely dropped). From Table 6 it looks like random sampling is actually slightly better than adversarial training in terms of robustness to CharSwap attacks in the Transformer model. Moreover, the benefit of adversarial rather than random sampling is quite small in the LSTM model as well. This could be made more clear in the text. It would be interesting to see how adversarial training with the CharSwap method fares against the unconstrained and kNN attacks in table 6.
assistant
 The authors provide a natural definition of adversarial examples for natural language transduction (meaning-preserving on source side while meaning-destroying on target side) and a human judgment task to measure it. They then investigate three different ways of generating adversarial examples and show that a metric based on character n-gram overlap (chrF) has a stronger correlation with human judgment. Finally, they show that adversarial training with the attack most consistent with the introduced meaning-preservation criteria results in improved robustness to this type of attack without degradation in the non-adversarial setting. Overall this is a strong paper. It is well structured, the problem studied is highly interesting and the proposed meaning-preserving criteria and human judgement will be useful to anyone interested in adversarial attacks for natural language. While the studied attack methods are fairly primitive, the empirical results are still interesting. Comments --------------- I wish the authors would include experiments with CharSwap where OOV is not forced as I*m not sure the assumption that OOV is more meaning-destroying in the target side is necessarily true (one could also argue that since the models are already trained with OOV words, they may be more robust to OOV words than in-vocabulary words in the wrong context). It would be nice to add correlation for each type of constraint as well to Table 2. The result would be even stronger if the experiment was replicated in the opposite direction or for another language pair as well. I don*t understand why the adversarial output in the second example in table 4 has a RDchrF of zero (the word July is completely dropped). From Table 6 it looks like random sampling is actually slightly better than adversarial training in terms of robustness to CharSwap attacks in the Transformer model. Moreover, the benefit of adversarial rather than random sampling is quite small in the LSTM model as well. This could be made more clear in the text. It would be interesting to see how adversarial training with the CharSwap method fares against the unconstrained and kNN attacks in table 6.