Expectations over Unspoken Alternatives Predict Pragmatic Inferences (2023)

Abstract: Scalar inferences (SI) are a signature example of how humans interpret language based on unspoken alternatives. While empirical studies have demonstrated that human SI rates are highly variable—both within instances of a single scale, and across different scales—there have been few proposals that quantitatively explain both cross- and within-scale variation. Furthermore, while it is generally assumed that SIs arise through reasoning about unspoken alternatives, it remains debated whether humans reason about alternatives as linguistic forms, or at the level of concepts. Here, we test a shared mechanism explaining SI rates within and across scales: context-driven expectations about the unspoken alternatives. Using neural language models to approximate human predictive distributions, we find that SI rates are captured by the expectedness of the strong scalemate as an alternative. Crucially, however, expectedness robustly predicts cross-scale variation only under a meaning-based view of alternatives. Our results suggest that pragmatic inferences arise from context-driven expectations over alternatives, and these expectations operate at the level of concepts.1

## introduction much of the richness of linguistic meaning arises from what is left unsaid (e.g., @xcite @xcite . for example, if alice says ''some of the students passed the exam'', bob can infer that alice means not all students passed the exam, even though alice's utterance would still be logically true if all students had passed. one explanation of this inference is that bob reasons about the unspoken alternatives code and data can be found at: https://github .com/jennhu/expectations-over-alternatives . si has often been treated as a categorical phenomenon: when a speaker utters a weaker (less informative) item on a scale, a listener rules out the meaning of stronger (more informative) items on that scale (e.g., @xcite . however, empirical studies have demonstrated substantial variability in the rates at which humans draw sis, both within instances of a single scale @xcite @xcite and across scales formed by different lexical items (e.g., @xcite @xcite @xcite . for example, consider the following instances of the scale some, all : here, we investigate a shared account of si rates within and across scales. since the alternatives are not explicitly produced (by definition), the listener has uncertainty over which alternatives the speaker could have used-and therefore, which strong scalemates ought to be negated through si. building upon constraint-based accounts of human language processing @xcite , we test the hypothesis that sis depend on the availability of alternatives, which depend on context-driven expectations maintained by the listener. for example, if a speaker says ''the movie was good'', the listener might predict that amazing is a more likely alternative than funny to the weak term good. an expectation-based view predicts that the listener would be thus be more likely to infer that the movie is not amazing (according to the speaker), and less likely to infer that the movie is not funny. however, while @xcite have argued that listeners maintain context-driven expectations over alternatives, these studies have primarily investigated a single scale ( some, all ) in small domains, arguing from qualitative patterns and in the absence of a formal theory. furthermore, while it is generally assumed that sis arise based on reasoning about unspoken alternatives, it remains debated whether humans reason about alternatives as linguistic structures (e.g., @xcite , or at the level of concepts (e.g., @xcite . returning to the earlier example, if the weak scalemate is good, listeners may reason about a concept like verygood instead of a specific linguistic expression like amazing. in this sense, the listener's uncertainty about alternatives might arise from uncertainty about both the scale itself (is the speaker implying the plot wasn't amazing, or that the jokes weren't funny?), as well as the exact word forms under consideration by the speaker (is the speaker implying the movie wasn't amazing, fantastic, or wonderful?). despite theoretical debates about the nature of alternatives, however, the role of concept-based alternatives in si has not been tested in a systematic, quantitative way. we provide a formalization of an expectationbased account of alternatives and test it on both string-based and concept-based views of alternatives. instead of empirically estimating human expectations over alternatives (cf. @xcite , we use neural language models as an approximation, which allows us to generate predictions for arbitrary sentences and contexts. we test the account's predictions on human si rates within the some, all scale @xcite , and across 148 scales from four datasets @xcite @xcite . we find support for the expectation-based account, and also provide the first evidence that concept-based alternatives may be underlying a wide range of sis. our results suggest that pragmatic inferences may arise from context-driven expectations over unspoken alternatives, and these expectations operate at the level of concepts. ## an expectation-based account of si theoretically, it is the set of alternative utterances-utterances that the speaker could have used, but didn't-that drive scalar implicature, and in principle every possible utterance in a language might be an alternative to every other. however, at an algorithmic level @xcite , it would be intractable for listeners to perform inference over this entire set. furthermore, the signature pattern of si would not arise without restrictions on the alternatives: otherwise, ''[weak], but not @xcite '' would both be alternatives to ''[weak]'', leading to contradictory inferences without a mechanism for breaking symmetry @xcite @xcite . to solve this symmetry problem, some approaches restrict alternatives based on structural complexity through grammar-internal mechanisms (e.g., @xcite . however, these theories do not capture the uncertainty that listeners maintain, and are difficult to test quantitatively. here, we test the view that listeners form probabilistic expectations over alternatives, given information from their interaction with the speaker. in the remainder of this section, we first discuss the conceptual predictions of an expectation-based account of si, and then describe how we operationalize these predictions using neural language models. suppose that a listener hears a sentence with a weak scalar term [weak] (e.g., ''this student is intelligent''). to rule out the meaning of a particular strong scalemate [strong] (e.g., the student is not brilliant), the listener must have reason to believe that the speaker would have said [strong] if they had intended to convey the strong meaning. however, since the alternatives are not explicitly produced, the listener has some degree of uncertainty over what alternatives were considered by the speaker. if it is likely that the speaker would have said @xcite to convey the strong meaning, then their choice to say [weak] suggests that they did not have grounds to say [strong]-and thus, an si should be more likely to arise. the key question, then, is how listeners estimate which alternatives are likely to be considered by the speaker. an expectation-based account proposes that listeners integrate contextual and grammatical cues to maintain probabilistic expectations over these alternatives. a scalemate that is more probable (given these cues) should be more likely to enter the scalar inference computation. thus, this account predicts that the more expected the strong scalemate is as an alternative to the weak scalemate, the higher si rates should be. ## discussion we tested a shared mechanism explaining variation in si rates across scales and within some, all , based on the hypothesis that humans maintain context-driven expectations about unspoken alternatives @xcite . we operationalized this in two ways using neural language models: the expectedness of a linguistic alternative as a scalemate (string-based surprisal), and the expectedness of a conceptual alternative (weighted average surprisal). we found that for both within-scale and cross-scale variation, expectedness captures human si rates. crucially, however, expectedness of the strong scalemate is a robust predictor of cross-scale variation only under a conceptual view of alternatives @xcite . our results support the idea that the strength of pragmatic inferences depends on the availability of alternatives, which depends on in-context predictability. one open question is the source of variability across the tested human behavioral datasets-in particular, the lack of surprisal effect for van tiel et al.'s data (section 5.4). while we cannot be certain about why the results vary, we identified a few differences that might affect data quality across datasets (see we adopt this view for several reasons. first, it could be the case that the context does not provide enough information for the listener to narrow down alternatives. returning to the running example, ''the soup is'' could be followed by many continuations, some potentially relating to the taste or size of the soup in addition to its temperature. taking the weak scalar term ''warm'' into account allows the listener to restrict the relevant alternatives to a smaller, more tractable set, which presents an algorithmic solution to the computationally challenging inference problem. however, the underinformativity of the context may be a problem unique to the simple stimuli used in the behavioral experiments. it is plausible that listeners could sufficiently restrict alternative sets given more naturalistic contexts, which likely provide more cues to the question under discussion @xcite . in addition, there could be cues from [weak] that provide information about likely alternatives, independent of the context. for example, listeners might prefer strong scalemates that match [weak] in register or formality, or in shared phonological features. this motivates why we chose template (1) to measure expectations over alternatives, instead of @xcite . however, the extent to which listeners tune their predictions based on [weak] above and beyond the context remains an open empirical question.