I really like the idea of seeing how far you can get with understanding filler-gap interpretation, given very naive ideas about language structure (i.e., linear w.r.t. verb position, as vS&E2014 do). Even if it’s not this particular shallow representation (and instead maybe a syntactic skeleton like the kind Gutman et al. 2014 talked about), the idea of what a “good enough” representation can do for scaffolding other acquisition processes is something near and dear to my heart.
One niggling thing — given that vS&M2014 say that this model represents a learner between 15 and 25-30 months, it’s likely the syntactic knowledge is vastly more sophisticated at the end of the learning (i.e., ~25 months). So the assumptions of simplified syntactic input may not be as necessary (or appropriate) later on in development. More generally, this kind of extended modeling timeline makes me want more integration with the kind of acquisition framework of Lidz & Gagliardi (2015), which incorporates developing knowledge into the model’s input & inference.
One other thing I really appreciated in this paper was how much they strove to connect the modeling assumptions and evaluation with developmental trajectory data. We can argue about the implementation of the information those empirical data provide, sure, but at least vS&E2014 are trying to seriously incorporate the known facts so that we can get an informative model.
Other specific thoughts:
(1) At the end of section 3, vS&E2014 say the model “assumes that semantic roles have a one-to-one correspondence with nouns in a sentence”. So…is it surprising that “A and B gorped” is interpreted as “A gorped B” since it’s built into the model to begin with? That is, this misinterpretation is exactly what a one-to-one mapping would predict - A and B don’t get the same role (subject/agent) because only one of them can get the role. Unless I misunderstood what the one-to-one correspondence is doing.
(2) I wasn’t quite sure about this assumption mentioned in section 3: “To handle recursion, this work assumes children treat the final verb in each sentence as the main verb…”. So in the example in Table 1, “Susan said John gave (the) girl (a) book”, “gave” is the “main” verb because…why? Why not just break the sentence up by verbs anyway? (That is, “said” would get positions relative to it and “gave” would get positions relative to it, and they might overlap, but…okay?) Is this assumption maybe doing some other kind of work, like with respect to where gaps tend to be?
(3) If I’m understanding the evaluation in section 5 correctly, it seems that semantic roles commonly associated with subject and object (i.e., agent, patient, etc. depending on the specific verb) are automatically assigned by the model. I think this works for standard transitive and intransitive verbs really well, but I wonder about accusatives (fall, melt, freeze, etc.) where the subject is actually the “done-to” thing (i.e., Theme or Patient, so the event is actually affecting that thing). This is something that would be available if you had observable conceptual information (i.e., you could observe the event the utterance refers to and determine the role that participant plays in the event).
Practically speaking, it means the model assigning “theme/patient” to the subject position (preverbal) would be correct for unaccusatives. But I don’t think the current model does this - in fact, if it just uses “subject” and “object” to stand in for thematic/conceptual roles, the “correct” assignment would be the subject NP of unaccusatives as an “object” (Theme/Patient)….which would be counted as incorrect for this model. (Unless the BabySRL corpus that vS&E2014 used labels thematic roles and not just grammatical roles? It was a bit unclear.) I guess the broader issue is the complexity of different predicate types, and the fact that there isn’t a single mapping that works for all of them.
This came up again for me in section 6 when vS&E2014 compare their results to the competing BabySRL model and they note that when given a NV frame (like with intransitives or unaccusatives), BabySRL labels the lone NP as an “object” 30 or 40% of the time. If the verb is an unaccusative, this would actually be correct (again, assuming “object” maps to “patient” or “theme”).
(4) Section 6: “…these observations suggest that any linear classifier which relies on positioning features will have difficulties modeling filler-gap acquisition” — including the model here? It seemed like the one vS&E2014 used captured the filler-gap interpretations effects they were after, and yet relied on positioning features (relative to the main verb).
Gutman, Ariel, Isabelle Dautriche, Benoit Crabbe, & Anne Christophe 2015. Bootstrapping the Syntactic Bootstrapper: Probabilistic Labeling of Prosodic Phrases, Language Acquisition, 22(3), 285-309.
Lidz, J., & Gagliardi, A. (2015). How Nature Meets Nurture: Universal Grammar and Statistical Learning. Annu. Rev. Linguist., 1(1), 333-353.