Why do human languages have homophones?

Trott, Sean; Bergen, Benjamin

doi:10.1016/j.cognition.2020.104449

Cited by 18 publications

(26 citation statements)

References 39 publications

(60 reference statements)

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“…Laplace smoothing is a popular choice, being used in a number of recent works in computational linguistics (e.g. Dautriche et al, 2017a;Trott and Bergen, 2020). However, it is perhaps the simplest of such regularisation techniques, and usually leads to much weaker empirical performances than, e.g., Kneyser-Ney (Ney et al, 1994).…”

Section: Experimental Methodologymentioning

confidence: 99%

“…In this work, we contribute to the debate surrounding a specific form of lexical ambiguity in which a wordform shares multiple unrelated meanings: homophony. 1 While the quantitative study of homophony dates back to (at least) Zipf (1949), recently, Trott and Bergen (2020) proposed a new explanation for the higher rate of homophony amongst good, i.e. short or phonotactically well-formed, wordforms: if words were sampled i.i.d.…”

Section: Introductionmentioning

confidence: 99%

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On Homophony and Rényi Entropy

Pimentel¹,

Meister²,

Teufel³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Homophony's widespread presence in natural languages is a controversial topic. Recent theories of language optimality have tried to justify its prevalence, despite its negative effects on cognitive processing time; e.g., Piantadosi et al. (2012) argued homophony enables the reuse of efficient wordforms and is thus beneficial for languages. This hypothesis has recently been challenged by Trott and Bergen (2020), who posit that good wordforms are more often homophonous simply because they are more phonotactically probable. In this paper, we join in on the debate. We first propose a new information-theoretic quantification of a language's homophony: the sample Rényi entropy. Then, we use this quantification to revisit Trott and Bergen's claims. While their point is theoretically sound, a specific methodological issue in their experiments raises doubts about their results. After addressing this issue, we find no clear pressure either towards or against homophony-a much more nuanced result than either Piantadosi et al.'s or Trott and Bergen's findings.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Homophony and Rényi Entropy

Pimentel¹,

Meister²,

Teufel³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…Take, for instance, several recent publications in this very journal with titles such as, "Why are social interactions found easily in visual search tasks" (Vestner et al, 2020), "Why do human languages have homophones?" (Trott & Bergen, 2020), and "Gamble evaluation and evoked reference sets: why adding a small loss to a gamble increases its attractiveness" (McKenzie & Sher, 2020). In each of these cases, the authors might intend to ask how it is that some phenomenon arises or what purpose the phenomenon serves in broader cognition.…”

Section: Understanding 'Why'mentioning

confidence: 99%

Understanding ‘Why’: How implicit questions shape explanation preferences

Joo¹,

Yousif²,

Keil³

2021

Preprint

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‘Why’ questions are semantically ambiguous. A question like “Why is the sky blue?” can be rephrased as either a ‘how’ (“How did the sky get its blue color?”) or a ‘purpose’ question (“What is the purpose of the sky being blue?”). This semantic ambiguity allows us to seek many kinds of information with the same ‘why’ question. As a result, ‘why’ questions have often been used to investigate people’s explanation preferences. From such work, we know that people will often prefer teleological over mechanistic explanations—a tendency that has been linked to many broader theories of human cognition. But are ‘why’ questions pragmatically ambiguous? You may, for instance, have a specific expectation about what “Why is the sky blue?” was really meaning to ask. Here, we show that (a) people have clear, domain-specific expectations about what specific questions are implied by ambiguous ‘why’ questions; (b) people have clear preferences for certain kinds of questions over others; and (c) there is a direct link between implicit questions and explanation preferences. Thus not only is “why” pragmatically unambiguous, but these specific expectations may shape known explanation preferences. To test this view, we finally show that people endorse teleological answers even when they are explicitly non-explanatory. In other words, people may sometimes prefer teleological answers because they interpret ‘why’ questions as ‘purpose’ questions (rather than as ‘how’ questions) and teleological explanations may simply better address these questions. We discuss how understanding ‘why’ may reshape our understanding of people’s explanation preferences and their consequences.

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“…5 Lexical ambiguity allows the mapping of multiple meanings to the same wordform and, in doing so, it enables the preferential re-use of short words (Piantadosi et al, 2012). Thus, the mapping of multiple meanings to the same form could be a source of efficiency in the lexicon (Fenk-Oczlon and Fenk, 2008;Ferrer-i-Cancho and Vitevitch, 2018;Casas et al, 2019;Trott and Bergen, 2020;Xu et al, 2020). Nonetheless, we do not treat it explicitly here.…”

Section: Words As Meaningsmentioning

confidence: 99%

How (Non-)Optimal is the Lexicon?

Pimentel¹,

Nikkarinen²,

Mahowald³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The mapping of lexical meanings to wordforms is a major feature of natural languages. While usage pressures might assign short words to frequent meanings (Zipf's law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world's languages. Despite their importance in shaping lexical structure, the relative contributions of these factors have not been fully quantified. Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints. Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon's optimality and to explore the relative costs of major constraints on natural codes. We find that (compositional) morphology and graphotactics can sufficiently account for most of the complexity of natural codes-as measured by code length.

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Why do human languages have homophones?

Cited by 18 publications

References 39 publications

On Homophony and Rényi Entropy

On Homophony and Rényi Entropy

Understanding ‘Why’: How implicit questions shape explanation preferences

How (Non-)Optimal is the Lexicon?

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