REFERENTIAL CHOICE: FACTORS AND MODELING Andrej A. Kibrik, Mariya V. Khudyakova, Grigoriy B. Dobrov, and Anastasia S. Linnik Night Whites SPb February 28, 2014

2 22 Referential choice in discourse When a speaker needs to mention (or refer to) a specific, definite referent, s/he chooses between several options, including: Full noun phrase Proper name (e.g. Peter) Description = common noun (with or without modifiers) (e.g. the tzar) Mix: Peter the Great Reduced NP, particularly a third person pronoun (e.g. he)

3 Example The Victorian house that Ms. Johnson is inspecting has been deemed unsafe by town officials. But she asks a workman toting the bricks from the lawn to give her a boost through an open first-floor window. Once inside, she spends nearly four hours Ø measuring and diagramming each room in the 80-year-old house, Ø gathering enough information to Ø estimate what it would cost to rebuild it. She snaps photos of the buckled floors and the plaster that has fallen away from the walls. DescriptionProper namePronoun Zero

4 Research question How is referential choice made?

5 Why is this question important? Reference is among the most basic cognitive operations performed by language users Reference constitutes a lions share of all information in natural communication Consider text manipulation according to the method of Biber et al. 1999:

6 Referential expressions marked in green The Victorian house that Ms. Johnson is inspecting has been deemed unsafe by town officials. But she asks a workman toting the bricks from the lawn to give her a boost through an open first-floor window.

7 Referential expressions removed The Victorian house that Ms. Johnson is inspecting has been deemed unsafe by town officials. But she asks a workman toting the bricks from the lawn to give her a boost through an open first-floor window.

8 Referential expressions kept The Victorian house that Ms. Johnson is inspecting has been deemed unsafe by town officials. But she asks a workman toting the bricks from the lawn to give her a boost through an open first-floor window.

9 Types of referential devices: levels of granularity We mostly concentrate on the two upper levels in this hierarchy REG tradition: most attention to varieties of descriptive full NPs

10 Multi-factorial character of referential choice Multiple factors of referential choice Distance to antecedent Along the linear discourse structure (Givón) Along the hierarchical discourse structure (Fox, Kibrik) Antecedent role (Centering theory) Referent animacy (Dahl) Protagonisthood (Grimes) Properties of the discourse context Properties of the referent

11 Cognitive multi-factorial model of referential choice Discourse context Referent activation in working memory Referents properties Referential choice Factors of referential choice

12 Rhetorical distance Distance along the hierarchical discourse structure between the current point in discourse, where referential choice is to be made the antecedent Measured in elementary discourse units roughly equaling clauses Rhetorical structure theory by Mann and Thompson (RST) Very important factor RST Discourse Treebank corpus (Marcu et al.)

13 Example of a rhetorical graph from RST Discourse Treebank

14 RefRhet and MoRA RST Discourse Treebank + our annotation = RefRhet corpus Subcorpus RefRhet 3 ( ) Annotation scheme MoRA (Moscow Referential Annotation)

15 RefRhet 3 64 texts 6294 markables 1852 anaphor-antecedent pairs 475 pronouns 1377 full NPs 706 descriptions 671 proper names

16 Candidate factors of ref. choice Some values are drawn from MoRA annotation Some other are computed automatically Factor-predicted variable Discourse context

17 Windows of the MMAX2 program

18 Some properties of the MoRA scheme Wide range of activation factors and their values E.g. multiple values of the grammatical role factor Annotation of groups complex markables serving as antecedents and-coordinate or-coordinate prepositional (children with their parents) discontinuous

19 A discontinuous group

20 Tasks for machine learning Candidate factors: All potential parameters implemented in corpus annotation Factor-predicted variable: Form of referential expression (np_form) Two-way task: Full NP vs. pronoun Three-way task: Definite description vs. proper name vs. pronoun Accuracy maximization: Ratio of correct predictions to the overall number of instances

21 Machine learning methods (Weka, a data mining system) Logical algorithms Decision trees (C4.5) Decision rules (JRip) Logistic regression Compositions Boosting Bagging Quality control – the cross-validation method

22 Results of machine learning on RefRhet 3 and MoRA Algorithm Accuracy two-way Accuracy two-way (2012) Accuracy three-way Baseline (frequency of the most common ref. option) 74,4% 37,9% Logistic regression87,2%71,3% Decision tree algorithm93,7% 86,1% 74,0% Bagging89,4% 88,0% 76,1% Boosting89,5% 86,2% 74,0%

23 Non-categorical referential choice (Kibrik 1999) min Referent activation max Cognitive plane: graded variable Linguistic plane: binary variable full NP Peter pronoun he

24 Non-categorical referential choice In many instances, more than one referential options can be used Referential choice is less than fully categorical (cf. Belz & Varges 2007, van Deemter et al. 2012: 173–179) In the intermediate activation instances both the original text author and the algorithm: more or less randomly make a categorical decision at the linguistic plane those decisions do not have to always coincide Therefore, no model can predict the actual referential choice with 100% accuracy

25 Experiment: Understanding (allegedly non-categorical) referential expressions 9 texts, in which the algorithms have diverged in their prediction from the original referential choice 9 original texts (proper name) and 9 altered texts (pronoun) distributed between 2 experimental lists 60 participants 1 experimental question + 2 control question If the instances of divergence are explained by intermediate referent activation, the accuracy in experimental questions should not be lower than the accuracy in control questions 25

26 Control questions – 84% Questions to proper names – 84% Questions to pronouns – 75% If we exclude questions #2 and #5, then the accuracy for questions to pronouns is 80%, not differing significantly from control and PN questions In general, the algorithm diverges from the original in the places where that is acceptable, that is, referent activation is intermediate Experiment: results 26

27 Non-categorical referential choice Sometimes referential choice allows more than one option A proper model of referential choice must account for this property of human speakers Our modeling procedures actually conform to this requirement

28 Further studies Explore logistic regressions ability to evaluate the certainty of prediction and attempt to correlate that with the humans assessment of non-categorical referential choice as well as with the theoretical notion of intermediate referent activation Cheap data modeling Secondary referential options, such as demonstrative descriptions Genres and referential choice

29 Conclusions Multi-factorial approach Corpus large enough for machine-learning modeling Results of prediction close to theoretical maximum Account of the non-deterministic character of referential choice This approach can be applied to a wide range of other linguistic choices

30 Thank you for your attention