Complexity/informativeness trade-off in the domain of indefinite pronouns

Abstract

The vocabulary of human languages has been argued to support efficient communication by optimizing the trade-off between complexity and informativeness (Kemp & Regier 2012). The argument has been based on cross-linguistic analyses of vocabulary in semantic domains of content words such as kinship, color, and number terms. The present work extends this analysis to a category of function words: indefinite pronouns (e.g. someone, anyone, no-one, cf. Haspelmath 2001). We build on previous work to establish the meaning space and featural make-up for indefinite pronouns, and show that indefinite pronoun systems across languages optimize the complexity/informativeness trade-off. This demonstrates that pressures for efficient communication shape both content and function word categories, thus tying in with the conclusions of recent work on quantifiers by Steinert-Threlkeld (2019). Furthermore, we argue that the trade-off may explain some of the universal properties of indefinite pronouns, thus reducing the explanatory load for linguistic theories.

The forms and meanings of grammatical markers support efficient communication

“…We presented an account of grammatical marking which suggests that number, tense and evidentiality systems across languages achieve efficient tradeoffs between informativeness and simplicity. Our results align with related results previously reported for domains including color naming (Zaslavsky et al, 2018), kin naming (Kemp & Regier, 2012), quantifiers (Steinert-Threlkeld, 2020), numeral systems (Xu, Liu, & Regier, 2020), and indefinite pronouns (Denić, Steinert-Threlkeld, & Szymanik, 2020), and with a broader literature that characterizes ways in which language supports efficient communication (Gibson et al, 2019). Within this literature there are studies that focus on meaning (e.g., Kemp et al, 2018) and studies that focus on form (e.g., Piantadosi et al, 2011), but few that address both meaning and form (e.g., Dautriche, Mahowald, Gibson, & Piantadosi, 2017;Tamariz, 2008).…”

“…The results therefore suggest that our model accounts better for attested systems than a similar approach that relies on inventory complexity. Some previous work that explores tradeoffs between information loss and complexity has relied on descriptive complexity (Kemp & Regier, 2012;Denić et al, 2020;Steinert-Threlkeld, 2020), and a similar approach could be applied here. Previous work in this tradition typically commits to a domain-specific representation language, and complexity is then defined as the length of the shortest representation in that language.…”

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

“…On the one hand, the first experiment shows that the semantic typology of quantifiers, a functional part of the lexicon, can be explained by efficient communication. Building on Reference [26], this is the first of a growing number of results suggesting that the same pressures shape semantic systems in domains of function words in addition to content words, from indefinites [27] to logical connectives [28,29] to person systems [30]. More provocatively, the two experimental results, taken together, suggest that semantic universals may be epiphenomenal: while empirically true, they arise as a consequence of a fundamental pressure for efficient communication and do not themselves stand in need of independent explanation.…”

“…Since that pioneering work, similar analyses have been carried out for color terms [20,44], container terms [45], and numeral systems [46]. Starting with the precursor to the present paper, Reference [26], the framework has also been applied to several domains of function words, from indefinites [27] to logical connectives [28,29] to person systems [30]. These studies suggest that efficient communication shapes the structure of the semantic systems of the world's languages, across both content and function words.…”

Conceptual Similarity and Communicative Need Shape Colexification: An Experimental Study

“…The former relates to ease of learning; while the latter relates to low information loss or communicative cost (the terminology and foci vary between authors and disciplines, cf. Beckner, Pierrehumbert, & Hay, 2006; Bentz, Alikaniotis, Cysouw, & Ferrer‐i Cancho, 2017; Carr et al., 2020; Carstensen, Xu, Smith, & Regier, 2015; Denić, Steinert‐Threlkeld, & Szymanik, 2021; Fedzechkina et al., 2012; Gasser, 2004; Haspelmath, 2021; Kemp & Regier, 2012; Kirby & Hurford, 2002; Kirby et al., 2015; Nölle et al., 2018; Smith, 2020; Steinert‐Threlkeld & Szymanik, 2020; Uegaki (in preparation); Winters et al., 2015; Zaslavsky, Regier, Tishby, & Kemp, 2019b). Thesestudies have yielded converging evidence that languages which are learned and used in communication—the real‐world ones, the artificial ones grown in the lab, as well as those evolved by computational agents—all aspire to balance these two pressures, ending up somewhere along the optimal frontier.…”