============================================================================ COLING 2016 Reviews for Submission #383 ============================================================================ Title: Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition Authors: Mehmet Ali Yatbaz, Volkan Cirik, Aylin Küntay and Deniz Yuret ============================================================================ REVIEWER #1 ============================================================================ --------------------------------------------------------------------------- Reviewer's Scores --------------------------------------------------------------------------- Relevance: 2 Originality: 2 Technical correctness / soundness: 2 Readability and clarity: 2 Meaningful comparison: 2 Substance: 2 Impact of ideas: 2 Impact of resources: 1 Overall recommendation: 2 Reviewer Confidence: 3 --------------------------------------------------------------------------- Comments --------------------------------------------------------------------------- This paper suggests that performance of the prediction of a syntactic category of a word in CHILDS corpora performs better with the model of the pattern with a slot (i.e. described as "a * b" in the article) than using a model separating the pattern heads ('aX') and the tails ('Xb') (i.e. described as "aX+Xb"). The performance of "a*b" being better than for "aX + Xb" in prediction seemed obvious to me in the presented experiment, since as in Figure 1, the information used for prediction in the former is larger than for the latter. In this sense, the comparison performed in this paper is not a "fair comparison". Maybe I misread, but if so, then authors must make sure to convince the readers that they are conducting a fair comparison. Therefore, I did not understand what claim is made in this article and my judgement with respect to COLING presentation unfortunately has to be reject. Apart from this main point, there are controversial issues related to this article. 1. The authors claim that "a*b" corresponds to "paradigmatic" representation, whereas "aX+Xb" to "syntagmatic", but uses of these two terms had better be reconsidered. In linguistics, paradigmatic and syntagmatic has the origin in F.de Saussure's definition. Calling "a*b"/"aX+Xb" as paradigmatic/syntagmatic seems too wide. 2. The authors refer to EMNLP-coNLL paper (the last reference), which has similar keywords in the title. If this refered paper is the authors' paper, then since the review process must be double blind, the authors should disclose the author names. If not, it must be shown what exactly is new of this paper compared with the reference. 3. The authors must explain more about their exact intent of this work. Do the authors want to engineer the CHILDES text? Then, the authors must explain the application needs and situate their work among previous work in the domain of engineering. Do the authors want to say from this paper, that children must be processing grammar through "a*b"? Then, the authors must explain the relation of their experiment how the experiment could relate to a cognitive claim. 4. Above all, I wonder whether this paper is appropriate for a presentation in the conferences of ACL community. Majority of the references are in the cognitive scientific domain. If behind this paper lies a cognitive question, then, it had better that they seek for a discussion in a conference dedicated in cognitive science domain. ============================================================================ REVIEWER #2 ============================================================================ --------------------------------------------------------------------------- Reviewer's Scores --------------------------------------------------------------------------- Relevance: 5 Originality: 3 Technical correctness / soundness: 4 Readability and clarity: 5 Meaningful comparison: 4 Substance: 3 Impact of ideas: 3 Impact of resources: 1 Overall recommendation: 4 Reviewer Confidence: 4 --------------------------------------------------------------------------- Comments --------------------------------------------------------------------------- ## General comments This paper presents an interesting study about the use of paradigmatic presentations for word category learning in the setting of natural language acquisition. The authors compare two models of word representations -- syntagmatic (where each word is represented based on its neighbors), and paradigmatic (where the word is represented using substitute words). The experiments presented here clearly show the benefit of paradigmatic representations, and this is a very interesting contribution to the field. However, I'm having a hard time understanding the dichotomy presented between these two representations: both seem to rely on context-based substitutability criteria, since even the paradigmatic presentation constructs the substitute words via use of n-grams which in turn are created by looking at the neighboring words of each target word. A related point is that there is a slight misrepresentation of Harris's distributional hypothesis in the example presented in the Introduction: in Harris's view, substitutability arrives through the use of all (total) environments of the words/morphemes; it is therefore "unfair" to compare the substitutes which have been created from a very large collection of environments (n-grams) with the local context of these two sentences. ## Specific issues/comments The first sentence of your introduction would find an entire section of the field in disagreement, namely researchers who subscribe to the constructivist approach who believe that children learn rules about individual words and only later form abstract syntactic categories. After the example presented an introduction, you mentioned that "the high probability substitute reflect both semantic and grammatical properties". As many papers in the distributional semantic network area have shown, this is not necessarily the case especially if you select a very small window from which you create your context representations (something which applies to the n-grams you use in this work), in which case they mostly reflect syntactic properties. How do you justify the claim that the Redington et al. method "lacks completeness"? There are a few missing references from the part of speech induction literature that would be interesting to include: Alex Clark's 2003 "Combining distributional and morphological information for part of speech induction", Christodoulopoulos et. al 2011 "A Bayesian Mixture Model for PoS Induction Using Multiple Features" and Blunsom and Cohn's "A Hierarchical Pitman−Yor Process HMM for Unsupervised Part of Speech Induction". In the input corpora that you used, did you remove the punctuation marks? They tend to provide a very strong distributional cue and are sometimes explicitly removed to better simulate the linguistic input of children. It is unclear what the contribution of Figure 1 is. Is it theoretically better or worse to have more units as input to the model? Is there any evidence to support these theoretical claims? Given the size of the training corpus for the n-gram model, I would suggest that the comparison to the syntagmatic models which are trained on significantly fewer words seems problematic. I would've liked to see a per-tag graph of the number of substitutes for each target word, since I would think that for some cases of word classes (interjections) this number would return a lot of noise. It would be better if you describe the connection between the results that you get in this work and those of St. Clair et al. 2010. ============================================================================ REVIEWER #3 ============================================================================ --------------------------------------------------------------------------- Reviewer's Scores --------------------------------------------------------------------------- Relevance: 4 Originality: 4 Technical correctness / soundness: 4 Readability and clarity: 4 Meaningful comparison: 3 Substance: 3 Impact of ideas: 3 Impact of resources: 1 Overall recommendation: 4 Reviewer Confidence: 3 --------------------------------------------------------------------------- Comments --------------------------------------------------------------------------- This paper describes a comparison of two approaches to POS tagging, one (syntagmatic) in which tags are assigned based on ngrams, and one (paradigmatic) in which tags are assigned based on possible lexical substitutions. The paper reports the latter (paradigmatic) gives higher accuracy. Overall it's an interesting result, but I have some reservations about how generally the results may be interpreted. First, if this model is to be taken as an acquisition model, as Section 1 seems to describe, I'm surprised the model uses supervised learning. Second, given this model is a supervised POS tagger, the accuracy results seem rather low compared to state-of-the-art sequence model taggers. This is potentially a problem because the gains due to paradigmatic modeling may not turn out to be significant in the presence of a more robust syntagmatic baseline. Minor comments: The last term in equation 3 should be "P(w_{n-1}|w^{n-2}_0)" (the zero is missing). Figures 2 and 3 should use bar graphs, since the slopes of lines between columns isn't interpretable. "patter(n)s" p. 5. You may want to cite work on syntagmatic and paradigmatic parser acquisition by Simon Dennis at U. Adelade in Australia.