Baayen et al (2018) (co-written by Jim Blevins, my undergraduate advisor from 25 years ago and still a mentor), in describing their own word-based, discriminative approach to morphology, contrast it with both widespread morpheme-based approaches and increasingly popular exponent-focused realizational approaches. I’ll leave a discussion of these different approaches to another time, but what is relevant to my previous post is this comment:

[morpheme-based and realizational analyses] may be of practical value, especially in the context of adult second language acquisition. It is less clear whether the corresponding theories, whose practical utility derives ultimately from their pedagogical origins, can be accorded any cognitive plausibility.

Note the distinction they are making between analyses of practical (adult SLA, pedagogical) value and cognitive plausibility.

Again, it’s not the point of this post to describe (much less assess) their arguments for why morphemes and exponents might not be cognitively plausible and what the alternative is, merely that they acknowledge certain analyses might be useful for pedagogical purposes independent of their cognitive plausibility (thereby agreeing with my psychological vs pedagogical distinction).

Perhaps cognitive would be another word for my psychological category.

They furthermore suggest:

Constructional schemata, inheritance, and mechanisms spelling out exponents are all products of descriptive traditions that evolved without any influence from research traditions in psychology. As a consequence, it is not self-evident that these notions would provide an adequate characterization of the representations and processes underlying comprehension and production. It seems particularly implausible that children would be motivated to replicate the descriptive scaffolding of [these] theoretical accounts…

Terms like “descriptive traditions” and “descriptive scaffolding of theoretical accounts” refer to what I had in mind with my synchronic category of analysis. Perhaps descriptive and theoretical would be other words for that category.

In a related paper, Baayen et al (2019), they talk about three possible responses to the challenge posed to linguistics (or at least linguistically-informed natural language processing) by the success of machine learning.

Αgain it’s outside the scope of this post to get into those details, but in short, their suggested possible responses are: (1) admit defeat, (2) claim the hidden layers reflect traditional linguistic representations, (3) rethink the nature of language processing in the brain. They go on to explore the third option in the context of morphology and the lexicon, stating that

the model that we propose here brings together several strands of research across theoretical morphology, psychology, and machine learning.

Note that this is essentially a claim that it’s possible to reconcile at least three of the different approaches I’ve outlined: the synchronic/description/theoretical, the cognitive/psychological, and the algorithmic/machine-learning.

(Missing here is any reference to diachrony or pedagogy, which I think they would agree are distinct approaches to what they are attempting to unify).

Now last week, I attended the Society for Computation in Linguistics meeting, coinciding with the big annual meeting of the Linguistic Society of America. One of the goals of SCiL is to build bridges from the NLP community to the linguistics community so it was of particular interest to me.

But again one of the big things that came up in multiple talks was distinct approaches: the approach of the NLP practitioners, often referred to as the engineering approach, and that of the linguists, often referred to as the scientific approach. At their most self-deprecating, the NLP practioners confessed their over-obsession with metrics on “tasks” and lack of regard for the underlying scientific “questions”. Noah Smith, in fact, joked that NLPers can annoy linguists by asking what their “task” is and linguists can annoy NLPers by asking what their “question” is.

The point of mentioning this is yet another example of a difference in approach and perspective.

Diachrony didn’t feature at all in either the Baayen/Blevins papers nor at SCiL, but certainly my other distinctions seem more broadly confirmed (albeit with alternative terminology). So I think we have:

• algorithmic / engineering / task-oriented
• diachronic
• synchronic / descriptive / theoretical
• psychological / cognitive
• pedagogical

Now this is not to say some of these approaches can’t be combined (as shown in the Baayen/Blevins papers). But even when one is attempting to combine some of them, I think it’s useful to acknowledge (a) the multiple approaches being combined; (b) other approaches with distinct goals and evaluation procedures that aren’t being consisdered but which may still be valuable in other contexts.

At the end of the day, I’m trying to turn arguments of the form “that isn’t a good theory/description/implementation/explanation of morphology” into a more nuanced “it probably isn’t good for this but it might be good for that”.

References

Baayen, R. H., Chuang, Y. Y., and Blevins, J. P. (2018). Inflectional morphology with linear mappings. The Mental Lexicon, 13 (2), 232-270.

Baayen, R. H., Chuang, Y. Y., Shafaei-Bajestan E., and Blevins, J. P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019, 1-39.