UID vs. other accounts of predictability-driven reduction

In the introduction to this chapter, I noted that currently there is no comprehen-sive theory of why specific omissions in fragments occur. However, there are two potential alternative explanations for part of the predictability effects on omis-sions in fragments that UID predicts. First, Ferreira & Dell (2000) analyze the optional omission of function words as driven solely by properties of language production. Second, information-theoretic measures like surprisal are probably often correlated to information-structural concepts like givenness, focus or top-icality. Therefore, I dedicate the remainder of this chapter to distinguishing the predictions of these approaches from the information-theoretic one that I pursue.

4.2.4.1 Availability-based production

Availability-based production (e.g. Bock 1987, Ferreira & Dell 2000) explains part of the data that I interpreted above as evidence for UID as the result of properties of language production.¹⁹ This approach relies on the difficulty of retrieving a lemma from memory. The idea is that speakers intend to produce speech fluently, and that the effortful retrieval of infrequent words delays speech production and thus results in disfluencies. These disfluencies are counterbalanced by inserting optional words that keep speech production fluent. As Ferreira & Dell (2000: 299) suggest, the insertion of such words has a similar effect as an “um”.

The main prediction of this approach is that insertions of optional words occur before unpredictable words, as Ferreira & Dell (2000) show for complementizers in English. UID predicts this too, but for a different reason: Realizing words be-fore unpredictable ones can reduce the surprisal of the latter and hence smooth peaks in the ID profile. However, availability-based production neither implies

19See also Jaeger & Buz (2017) for an overview and a comparison to UID.

that words that are themselves more predictable are more likely to be reduced, nor that predictable words tend to appear toward the beginning of the sentence.

Therefore, if they were empirically confirmed, these two predictions will provide evidence for UID. Since UID and availability-based production are theories about different aspects of language, as Jaeger & Buz (2017) note, they do not mutually exclude each other, but what matters in the context of my experiments is that data that cannot be explained by production preferences alone will support UID.

4.2.4.2 Information structure

Even though there is no fully worked-out information-structural account of frag-ment usage, information-structural and information-theoretic concepts are prob-ably often related. This might raise the question of whether surprisal is actually an artifact of information-structural notions like givenness or topicality. In what follows I show that the information-theoretic approach has explanatory, empiri-cal and methodologiempiri-cal advantages over a purely information-structural one.

Specifically sentential accounts of fragments assume a close relationship be-tween information-structural concepts such as focus, background, givenness or topicality and ellipsis: For instance, Merchant (2004a) requires elided expressions to be e-given and Reich (2007) and Weir (2014a) argue that only foci survive el-lipsis. The observation that in only expressions which are given can be elided re-minds of the finding that given referents tend to be prosodically less prominent (Féry & Ishihara 2009), and is in line with the analysis of ellipsis as an extreme form of reduction of prosodic prominence (Tancredi 1992).

This raises the question of whether information structure alone can explain the distribution of omissions or whether information-theoretic considerations are required in addition. From an information-structural perspective, the omission of predictable material might result from a tendency for predictable words to be given, or highly salient, whereas foci are less predictable.²⁰For instance, in the

20The question of whether surprisal is sometimes an artifact of information-structural concepts (which goes beyond the scope of this book) might be addressed with appropriate experimental studies, for instance by comparing focused expressions that differ in the number and likeli-hood of focus alternatives. While information theory predicts gradual effects of predictability, discrete concepts of focus and givenness predict a categorical difference between expressions that are focused and those that are not. Similarly, not all given expressions are equally likely to be talked about in upcoming discourse. For sluicing, Lemke et al. (forthcoming) show that even though in both contexts in (i) the person referred to bysomebodyis contextually given, participants are more likely to complete (ia) with a question referring to this referent (with whom).

(i) a. Mary was making out with somebody, but I don’t know … b. Mary painted her room with somebody, but I don’t know …

taxi example discussed above, a salient implicit QuD likeWhere do you want to go? might license ellipsis of everything but the focus, which corresponds to the wh-phrase in the answerTake me to the university. Since foci is defined by the presence of alternatives (Rooth 1992), they are necessarily less predictable than given constituents.

From a theoretical perspective, the main problem for a purely information-structural account of fragment usage is that information structure might license ellipsis, but it does not trigger it. Concepts like e-givenness determine which wordscanbe omitted, but obviously e-given words are not always omitted. There-fore, information structure can only explain why certain expressions cannot be omitted. In contrast, UID provides an account of why predictable words are preferably omitted. Furthermore, unlike UID, an information-structural account of fragment usage does not predict the insertion of redundancy before unpre-dictable words: The omission of a target word is licensed only by its own infor-mation-structural status (like e.g. (e-)givenness). UID additionally predicts that the likelihood of the word that follows a target word also determines whether the target word is omitted. This does not neglect that information structure can con-tribute to the predictability of a word being omitted, but information structure alone does not explain all of the effects that UID predicts.

Taken together, there is probably a high degree of overlap between the given-ness and surprisal of an expression, but only an information-theoretic account can explain why an expression whose omission is licensed is sometimes overtly realized. Nevertheless, it might be an interesting line of research to tease apart the predictions of an information-theoretic and an information-structural account in a controlled experimental setting.

in fragments

This chapter presents two experiments which investigate the predictions of the UID-based account of fragment usage.¹ This account makes the three testable predictions in (1). (1a) and (1b) are specific to UID, whereas (1c) can be analyzed either as an implication of (1a) and (1b) or as the result of efficient source coding.

(1) Predictions of UID on fragments

a. Avoid troughs: The more likely a word is in context, the more likely it is to be omitted (within the limits of grammar).

b. Avoid peaks: Uninformative words can be inserted before very infor-mative words in order to lower the surprisal of the latter (within the limits of grammar).

c. Densification: Shorter encodings, like fragments, are preferred in pre-dictive contexts.

The experiments investigate these issues at the case of discourse-initial frag-ments, which are the most uncontroversial instances of fragments. Since these fragments lack linguistic antecedents, the predictability of words within them mostly constrained by extralinguistic context. In order to quantify effects of ex-tralinguistic context on the predictability of utterances and words within them, both experiments rely on script knowledge (Schank & Abelson 1977) as an ap-proximation to extralinguistic context. Scripts trigger expectations about upcom-ing events and can be used to modulate the predictability of utterances that are related to these events. Furthermore, there is a crowdsourced corpus of script knowledge available that can be used to precisely quantify this predictability.

Both experiments manipulate the likelihood of utterances with context stories like (2), which are based on event probabilities extracted from the DeScript cor-pus of script knowledge (Wanzare et al. 2016). For instance, in context of this story, the most likely event to follow is that of pouring the pasta into the boiling

1Experiment 11 has been published in Lemke et al. (2021b) and experiment 12 has been published in Lemke et al. (2020) and Lemke et al. (2021a).

water, hence I assume that utterances that refer to this event, like (2a) are more likely than those referring to events which are unpredictable in the script corpus.

(2) Annika and Jenny want to cook pasta. Annika put a pot with water on the stove. Then she turned the stove on. After a few minutes, the water started to boil.

a. Pour the pasta into the pot. Predictable

b. Set the table. Unpredictable

Experiment 11 compares the acceptability of the sentences in (2a,b) to that of DP fragments derived from these sentences. Given clause (1c), UID predicts a rela-tively stronger preference for fragments in case of the predictable utterance (2a) than in case of (2b). Experiment 12 uses the same context stories to elicit a data set with a production task that is suitable for investigating the more fine-grained predictions in (1a) and (1b). The presence of ellipses in the data collected in ex-periment 12 requires a new method to estimate surprisal. My method extends the surprisal estimation technique proposed by Hale (2001) to elliptical data by allowing for an arbitrary number of omissions between words.

This chapter is organized as follows. In Section 5.1 I propose scripts as an ap-proximation to extralinguistic context and describe how I created experimental materials based on the DeScript corpus (Wanzare et al. 2016). Sections 5.2 and 5.3 present the experiments, and Section 5.4 summarizes the main results.