First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. On the plus side: the paper has an interesting and novel idea, and I believe the experimental investigation is competent and complete. On the minus side: I am skeptical that this idea could be a practical solution to MT, and I think your last translated-sentence example kind of shows the kind of weirdness that can result. In my mind, a solution has to be scalable in principle to long sentences, and I think it's clear that your method cannot. Q2: Please summarize your review in 1-2 sentences Accept but not very strongest accept.

Format is the same as Figure 2c&d. The peak correlation vs. segment duration curve tended to approach an asymptotic value at long segment durations (see Figure 2d). For simplicity, we estimated this asymptotic value for each unit by measuring the peak cross-context correlation across lag for the longest segment duration tested (2.48 seconds) (i.e., the rightmost values in the curves shown in Figure 2d). Convolutional layers have a maximum value of 1, as expected since they have a well-defined upper bound on their integration window. The LSTM layers also showed high maximum values (median correlation value across units was above 0.93 for all layers), indicating a mostly context-invariant response.

The use of topic modelling techniques, especially Latent Dirichlet Allocation (LDA) introduced by Blei et al. (2003), is growing fast. The methods find application in a broad variety of domains. In text-as-data applications, LDA enables the analysis of large collections of text in an unsupervised manner by uncovering latent structures behind the data. Given this increasing use of LDA as a standard tool for empirical analysis, also the interest in details of the method and, in particular, in parameter settings for its implementation is rising. Thus, since the introduction of the LDA approach in 2003 by Blei et al., different methodological components of LDA have already been studied in more detail as, for example, the choice of the number of topics (Cao et al., 2009; Mimno et al., 2011; Lewis and Grossetti, 2022; Bystrov et al., 2022a), hyper-parameter settings (Wallach et al., 2009), model design (e.g.