Redundancy and reduction: Speakers manage syntactic
information density
Torsten Jachmann
16.05.2014
T. Florian Jaeger (2010)
Seminar „Information Theoretic Approaches to the Study of Language “
So far• Frequent words have shorter linguistic forms
(Zipf)o Orthographic; PHONOLOGICAL
• Word length (phonemes/syllables) correlated with predictability
• Information is context dependento The more probable, the more redundant
• More predictable instances of the same word are produced shorter and with less phonological and phonetic detail
IdeaSpeakers manage the amount of information per
amount of linguistic signal (at choice points)
• Morphosyntactic:o E.g. auxiliary contractions
“he is” vs. “he’s”
IdeaSpeakers manage the amount of information per
amount of linguistic signal (at choice points)
• Syntactic:o E.g. optional that-mentioning
“This is the friend I told you about”vs.
“This is the friend that I told you about”
IdeaSpeakers manage the amount of information per
amount of linguistic signal (at choice points)
• Elide constituents:o E.g. optional argument and adjunct omission
“I already ate”vs.
“I already ate dinner”
IdeaSpeakers manage the amount of information per
amount of linguistic signal (at choice points)
• Production planning:o E.g. one or more clauses
“Next move the triangle over there”vs.
“Next take the triangle and move it over there”
IdeaSpeakers manage the amount of information per
amount of linguistic signal (at choice points)
• Other languages?o German:
“Er hat es verstanden” vs. “Er hat’s verstanden”(he understood it)
o Japanese:“ 行ってはダメ” vs. “ 行っちゃダメ”
Itte ha dame Iccha dame(you can’t go)
IdeaSpeakers manage the amount of information per
amount of linguistic signal (at choice points)
The form with less linguistic signal should be less preferred whenever the reducible unit encodes a
lot of information (in the context)
Uniform Information Density(UID)
Optimal:• On average each word adds the same amount of
information to what we already know• The rate of information transfer is close to the
channel capacity
❌Many constraints (grammar; learnability)
Uniform Information Density(UID)
Efficient:• Relative uniform information distribution where
possible• No continuous under- or overutilization of the
channel
?
UIDDefinitions:• Information density:
Information per time(articulatory detail is left out)
• Choice:Subconscious(existence of different ways to encode the intended message)
UIDExample:
UIDExample:
UIDGoals:• UID as a computational account of efficient
sentence production• Corpus-based studies are feasible and desirable
Corpus of spontaneous speech Naturally distributed data
Data• 7369 automatically extracted complement
clauses (CC) from “Paraphrase Stanford-Edinburgh LINK Switchboard Corpus” (Penn Treebank)
• - 144 (2%) falsely extracted
• - 71 (1%) rare matrix verbs extreme probabilities
DataFocusActually:I(CC onset|context) = -log p(CC|context) + -log p(onset|context,CC)
Here:I(CC|context) = -log p(CC|matrix verb lemma)
Data
Multilevel logit model
• Various factors (might) influence the outcome• Ability to include several (control) parameters in
one model• Contribution of each can be estimated
Why?• Natural (uncontrolled) data
ControlsDependency• Distance of matrix verb from CC onset “THAT”
o My boss thinks [I’m absolutely crazy.]o I agree with you [that, that a person’s heart can be changed.]
• Length of CC onset (including subject) “THAT”
• Length of CC remainder
Short sidetrackLength of CC remainder
• Language production is incremental (+ heuristic complexity estimates?)
ControlsAvailability
• Lower speech rate “THAT”
• preceding pause “THAT”
• initial disfluency “THAT”
ControlsAvailability• Type of CC subject
o It vs. Io Other PRO vs. aboveo Other NP vs. above
• Frequency of CC subject head• Subject identity
o Identical subject in matrix and CC ≈ “NONE”
ControlsAvailability• Word form similarity
o Demonstrative pronoun “that”o Demonstrative determiner “that”≈ “NONE”
• Frequency of matrix verbo Higher frequency “NONE”
ControlsAmbiguity avoidance• Possible garden path sentence “THAT” ❌
o Even unlikely cases were included“I guess (this doesn’t really have to do with…)”
ControlsMatrix• Position of matrix verb
o Further away from sentence-initial position “THAT”
• Matrix subjecto You vs. I “THAT”o Other PRO vs. above “THAT”o Other NP vs. above “THAT”
ControlsOthers• Random speakers intercept• Persistence
o Prime w/o that vs. no primeo Prime w/ that vs. above
• Gendero Male “NONE”
Information density• Clear significance (p < .0001)• High information density of the CC onset
use of “that”• Correlation with other predictors negligible• Contribution to the model’s likelihood is high• At least 15% of the model quality due to
information density• Single most important predictor
Information density
• Verbs’ subcategorization frequency as estimate for information density
• High CC-biases, low “that”-biases (e.g.: guess)• Low CC-biases, high “that”-biases (e.g.: worry)
Syntactic reduction is affected by information density
Results
Information density• Prediction:
UID can account for any type of reduction
Phonetic and phonological reduction• So far patterns align with this prediction• Availability account do not predict this
o But predict lengthening of words
Information density
Optional case markers (or copula)
• Languages with flexible word order• Japanese
ケーキが大好きだ vs. ケーキ大好きKeeki ga daisuki da keeki daisuki
I love cake
Information density
Reduced case markers
• Korean나는 독일 사람이야 vs. 난 독일 사람이야
Na neun togil saram iya Nan togil saram iya
I am German
Information density
Optional object clitics and other argument marking morphology
• Direct object clitics in Bulgariano Can’t be predicted by availability accounto Could be predicted by ambiguity avoiding
Information density
Contracted auxiliaries
• English“he’s” vs. “he is”
o Can’t be predicted by neither availability nor ambiguity avoidance
Information density
Ellipsis• Japanese
行きたいけど行けない vs. 行きたいけどikitai kedo ikenai ikitai kedo
I want to go but (I can’t go)
(¬ 行きたいけど ( 遅くなりそう ) I want to go, but I might be late) ikitai kedo osoku nari sou
Information density
Non-subject-extracted relative clause• Indefinite noun phrase < definite noun phrases• Light head nouns < heavy head nouns
(e.g. the way) (e.g. the priest)
“I like the way (that) it vibrates”
Information density
Whiz-deletion (BE)• Relativizer + auxiliary can be ommitted
“The smell (that is) released by a pig or a chicken farm is indescribable”
Information density
Object drop• Verbs with high selectional preference
“Tom ate.”vs.
“Tom saw …”
Information density• Many novel predictions across
oDifferent levels of linguistic productionso Languageso Types of alternations
• Per-word entropy of sentences should stay constant throughout discourseo Words with high information density (in the context and
discourse) should come later in the sentenceo A priori per-word entropy should increase
Grammaticalization• Might interfere with information density?
o Matrix subject “I” or “you”o Matrix verb “guess”, “think”, “say”, “know”, “mean”o Matrix verb in present tenseo Matrix clause was not embedded3033 cases remain
• Still highly significant (p < .0001)
UID may be a reason for grammaticalization
Noisy channel
• Base of UID• Audience design
o Speaker considers interlocutors’ knowledge and processor state to improve chance of successfully achieving their goal
• Modulating information density at choice points = rational strategy for efficient production
• UID minimizes processing difficulties
Corpus-based researchClaim: “Lack of balance and heterogeneity of data make findings unreliable”• Multilevel models• Avoidance of redundant predictors• If redundant residualization• Inter-speaker variance
+ ecological valid
Corpus-based research• Results easier extend to all of English• Many previous results replicate• Provides evidence for so far relatively
understudied effects (e.g. similarity avoidance)
• “effect size” needs to be taken with cautiono Not only strength of effects but also applicability
Ambiguity avoidance (garden path sentences) relatively rare
Questions and discussion