Sentence stress in presidential speeches A R T O A N T T I L A , T I - PowerPoint PPT Presentation

Sentence stress in presidential speeches A R T O A N T T I L A , T I M O T H Y D O Z AT , D A N I E L G A L B R A I T H , A N D N A O M I S H A P I R O 39th Annual Meeting of the DGfS Workshop on Prosody in Syntactic Encoding Saarbrücken, March 10, 2017

Why are sentences stressed the way they are? We are gòing to begìn to áct, begìnning TODÁY. (Ronald Reagan, Inaugural Address, January 20, 1981, Sentence 21)

Two kinds of sentence stress (Jespersen 1920: 212-222) (a) Mechanical stress, rhythmic stress, “physiological” (rhythmischer Druck, Einheitsdruck) (b) Meaningful stress, semantic stress, “psychological” (Wertdruck, Neuheitsdruck, Gegensatzdruck)

Semantic stress In the Gilmore Girls universe, Luke and Lorelai seemed inevitable. He served the coffee; she needed the coffee. (Correction: NEEDED the coffee.) Entertainment Weekly, November 17, 2016 http://www.ew.com/article/2016/11/17/gilmore-girls-luke- originally-woman

Mechanical stress How much did they pay you for participating in the experiment? Five francs. (Ladd 1996: 166)

Semantic stress is related to new information How is information packaged in the sentence? (a) Evenly spread (Uniform Information Density) (Levy and Jaeger 2007; Jaeger 2010) (b) Piles up towards the end (Communicative Dynamism) (Prague School, e.g., Firbas 1971) (c) Seeks out stress peaks (Stress-Information Alignment) (Bolinger 1957, 1972; Calhoun 2010; Cohen Priva 2012)

(a) Uniform Information Density

(b) Communicative Dynamism

(c) Stress-Information Alignment

Bolinger (1957: 235) “The recipe for reconciling the two functions [semantic and mechanical] is simple: the writer should make them coincide as nearly as he can by maneuvering the semantic heavy stress into the position of the mechanical loud stress; that is, toward the end.”

Stress-Information Alignment: A Proposal (a) Phrasal stress is assigned by syntax. (Chomsky, Halle, and Lukoff 1956; Chomsky and Halle 1968; Liberman and Prince 1977; Cinque 1993) (b) Information seeks out stress peaks, especially in good prose. (Bolinger 1957, 1972; Calhoun 2010; Cohen Priva 2012) stress = metrical strength

Plan of work 1. Find a text performed by an individual (script + audio + video). Inaugural addresses of Carter (1977), Reagan (1981), Bush Sr., (1989), Clinton (1993), Bush Jr. (2001), Obama (2009) 2. Assign mechanical stress to text by a computer. (MetricalTree, Dozat 2015-7) 3. Collect perceived stress judgments from native speakers. (MetricGold, Shapiro 2016-7) 4. Figure out to what extent perceived stress is explained by (i) the mechanical stress contour (ii) the distribution of information

Why is this interesting? Sentence stress is difficult to pin down: • not represented in writing • hard to measure by phonometric methods Yet it exists and is a hidden variable in many studies. Understanding sentence stress may help solve other linguistic puzzles.

Preview of findings 1. Both kinds of stress matter, but not uniformly: Noun and adjective stresses tend to be loud and mechanical. • Verb and function word stresses tend to be soft and semantic. • 2. Stress levels vary significantly across parts of speech: nouns > adjectives > verbs > function words

1. Predicting mechanical stress

Rules vs. variability The Nuclear Stress Rule (NSR) and the Compound Stress Rule (CSR) (Chomsky and Halle 1968, Liberman and Prince 1977, Cinque 1993) Sentence stress is variable. Why? • Free will • Ambiguity in lexical stress results in variation in phrasal stress: unstressed words (e.g., expletive it ) stress-ambiguous words (e.g., in , into ) stressed words (e.g., balloon ). Assumption: No variation in the phrasal stress rules themselves.

The stress rules (Chomsky and Halle 1968) The Nuclear Stress Rule (NSR): Assign [1 stress] to the rightmost vowel bearing the feature [1 stress]. Applies to phrases (NP, VP, AP, S). The Compound Stress Rule (CSR): Skip over the rightmost word and assign [1 stress] to the rightmost remaining [1 stress] vowel; if there is no [1 stress] to the left of the rightmost word, then try again without skipping the word. Applies to words (N, A, V).

Sample derivation [[[John's] [[[black] [board]] [eraser]]] [was stolen]] 1 1 1 1 1

First cycle [[[John's] [[[black] [board]] [eraser]]] [was stolen]] 1 1 1 1 1 [ 1 2 ]

Second cycle [[[John's] [[[black] [board]] [eraser]]] [was stolen]] 1 1 1 1 1 [ 1 2 ] [ 1 3 2 ]

Third cycle [[[John's] [[[black] [board]] [eraser]]] [was stolen]] 1 1 1 1 1 [ 1 2 ] [ 1 3 2 ] [ 2 1 4 3 ]

Final cycle [[[John's] [[[black] [board]] [eraser]]] [was stolen]] 1 1 1 1 1 [ 1 2 ] [ 1 3 2 ] [ 2 1 4 3 ] [ 3 2 5 4 1 ]

Liberman and Prince’s (1977) version The rules are defined on local syntactic trees as follows: In a configuration [A B], if the constituent is a phrase, B is strong (= NSR) if the constituent is a word, B is strong iff it branches (= CSR)

Syntax To assign phrasal stress we need a syntactic parse. • We used the Stanford Parser (Chen and Manning 2014) • http://nlp.stanford.edu/software/lex-parser.shtml •

Lexical stress (a) Unstressed words: it Unstressed tags: CC, PRP$, TO, UH, DT Unstressed deps: det, expl, cc, mark (b) Ambiguous words: this, that, these, those Ambiguous tags: MD, IN, PRP, WP$, PDT, WDT, WP, WRB Ambiguous deps: cop, neg, aux, auxpass (c) All other words, tags, and deps are stressed.

Phrasal stress A sentence has 2 n stress paths where n = the # of ambiguous words. Example: I ask you to share with me today the majesty of this moment (Richard Nixon, Inaugural Address, January 20, 1969, Sentence 2) Stress paths: 2 6 = 64

Phrasal stress Instead of examining all parses we limit ourselves to the following: Model 1: All ambiguous words  unstressed Model 2: All monosyllabic ambiguous words  unstressed; all polysyllabic ambiguous words  stressed Model 3: All ambiguous words  stressed Model 4: The ensemble model (= mean model)

the savings of many years in thousands of families are gone (FDR, Inaugural Address, March 4, 1933, Sentence 19)

2. Perceived stress

What is perceived stress? Perceived stress = syllable prominence felt by a native speaker Syllable prominence is “for the large part the work of the perceiver, generating his internal accent pattern on the basis of a strategy by which he assigns structures to the utterances. These structures, however, are not fabrications of the mind only, for they can be related to sound cues.” (van Katwijk 1974: 5, cited in Baart 1987: 4)

No attempt to eliminate variation Two native speakers may perceive the same prominence contour • differently: transcriptions reflect the grammar of the annotators. Variation is not noise, but data. We did not attempt to eliminate • variation from transcriptions, beyond loose annotation guidelines. Interannotator reliability is good (Cronbach’s alpha = 0.85). •

The Metric Gold annotation interface

Predicted stress (the mean model) “We are going to begin to act, beginning today” (Reagan 1981)

Perceived stress (Annotator 1) “We are going to begin to act, beginning today” (Reagan 1981)

Predicted vs. perceived stress (Annotator 1)

Predicted vs. perceived stress (Annotator 2)

The information-theoretic view “ The error of attributing to syntax what belongs to semantics comes from concentrating on the commonplace. In phrases like bóoks to write , wórk to do , clóthes to wear , fóod to eat , léssons to learn , gróceries to get - as they occur in most contexts - the verb is highly predictable: food is to eat, clothes are to wear, work is to do, lessons are to learn. Less predictable verbs are less likely to be de-accented-where one has léssons to learn , one will probably have pássages to mémorize . It is only incidental that the syntax favors one or the other accent pattern. ” (Bolinger 1972, pp. 634)

Approximating the information of a word doc.freq Document lexical frequency d.cp.1 Document conditional probability (unigram) d.cp.2 Document conditional probability (bigram) d.cp.3 Document conditional probability (trigram) d.inform.2 Document informativity (bigram) d.inform.3 Document informativity (trigram) corpus.freq Corpus lexical frequency c.cp.1 Corpus conditional probability (unigram) c.cp.2 Corpus conditional probability (bigram) c.cp.3 Corpus conditional probability (trigram) c.inform.2 Corpus informativity (bigram) c.inform.3 Corpus informativity (trigram)

Perceived stress vs. corpus frequency (Annotator 1)

Perceived stress vs. corpus frequency (Annotator 2)

Perceived stress vs. bigram informativity (Annotator 1)

Perceived stress vs. bigram informativity (Annotator 2)

Informativity vs. linear position (Prague school) (Pearson correlation = 0.02, p = 0.01643)

Information vs. predicted stress (Bolinger 1957) (Pearson correlation = 0.40, p < 2.2e-16)