Permutation Equivariant Models for Compositional Generalization in Language
Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of this paper is to hypothesize that language compositionality is a form of group-equivariance. Based on this hypothesis, we propose a set of tools for constructing equivariant sequence-to-sequence models. Throughout a variety of experiments on the SCAN tasks, we analyze the behavior of existing models under the lens of equivariance, and demonstrate that our equivariant architecture is able to achieve the type compositional generalization required in human language understanding.
This work focuses on learning equivariant representations and functions over input/output words for the purposes of SCAN task. Basically, the focus is on local equivariances (over vocabulary) such that the effect of replacing and action verb like RUN in the input with the verb JUMP causes a similar change in the output. However, effects requiring global equivariances like learning relationship between *twice* and *thrice*, or learning relationships between different kinds of conjunctions are not handled in this work. For learning equivariant functions over vocabulary, group convolutions are used at each step over vocabulary items in both the sequence encoder and decoder. The results on SCAN task are impressive for verb replacement based experiments and improve over other relevant baselines. Also, improvement is shown on another word replacement task (*around right*), which requires learning corresponding substitutions in output based on the word changes in the input. As expected, for experiments that require global equivariances or no equivariance (simple, length), the difference ion performance is not very pronounced over other baselines. While this paper does show that modelling effects of word substitution can be handled by the locally equivariant functions, it still cannot account for more complex generalization phenomena which are likely to be much more prevalent especially for domains dealing with natural language that are other than SCAN. Therefor, I think the applicability of the proposed equivariant architectures is rather limited if interesting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Permutation Equivariant Models for Compositional Generalization in Language
Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of this paper is to hypothesize that language compositionality is a form of group-equivariance. Based on this hypothesis, we propose a set of tools for constructing equivariant sequence-to-sequence models. Throughout a variety of experiments on the SCAN tasks, we analyze the behavior of existing models under the lens of equivariance, and demonstrate that our equivariant architecture is able to achieve the type compositional generalization required in human language understanding.
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 focuses on learning equivariant representations and functions over input/output words for the purposes of SCAN task. Basically, the focus is on local equivariances (over vocabulary) such that the effect of replacing and action verb like RUN in the input with the verb JUMP causes a similar change in the output. However, effects requiring global equivariances like learning relationship between *twice* and *thrice*, or learning relationships between different kinds of conjunctions are not handled in this work. For learning equivariant functions over vocabulary, group convolutions are used at each step over vocabulary items in both the sequence encoder and decoder. The results on SCAN task are impressive for verb replacement based experiments and improve over other relevant baselines. Also, improvement is shown on another word replacement task (*around right*), which requires learning corresponding substitutions in output based on the word changes in the input. As expected, for experiments that require global equivariances or no equivariance (simple, length), the difference ion performance is not very pronounced over other baselines. While this paper does show that modelling effects of word substitution can be handled by the locally equivariant functions, it still cannot account for more complex generalization phenomena which are likely to be much more prevalent especially for domains dealing with natural language that are other than SCAN. Therefor, I think the applicability of the proposed equivariant architectures is rather limited if interesting.
assistant
This work focuses on learning equivariant representations and functions over input/output words for the purposes of SCAN task. Basically, the focus is on local equivariances (over vocabulary) such that the effect of replacing and action verb like RUN in the input with the verb JUMP causes a similar change in the output. However, effects requiring global equivariances like learning relationship between *twice* and *thrice*, or learning relationships between different kinds of conjunctions are not handled in this work. For learning equivariant functions over vocabulary, group convolutions are used at each step over vocabulary items in both the sequence encoder and decoder. The results on SCAN task are impressive for verb replacement based experiments and improve over other relevant baselines. Also, improvement is shown on another word replacement task (*around right*), which requires learning corresponding substitutions in output based on the word changes in the input. As expected, for experiments that require global equivariances or no equivariance (simple, length), the difference ion performance is not very pronounced over other baselines. While this paper does show that modelling effects of word substitution can be handled by the locally equivariant functions, it still cannot account for more complex generalization phenomena which are likely to be much more prevalent especially for domains dealing with natural language that are other than SCAN. Therefor, I think the applicability of the proposed equivariant architectures is rather limited if interesting.