The Role of Background Knowledge in Sentence and Discourse Processing

1. The Role of Background Knowledge in Sentence and Discourse Processing Thesis Proposal Raluca Budiu February 9, 2000

2. Metaphors Time is money. • People from all cultures use metaphors on an every-day basis, irrespective of their level of education. • Language is full of frozen metaphors (Adam’s apple, leg of a table, etc.) • People understand (most) metaphors easily. Thesis proposal --- Raluca Budiu

3. “Mistakes” • People make mistakes when they speak. • Often people do not notice mistakes and can understand the message communicated: How many animals of each kind did Moses take on the ark? • It’s hard for people not to ignore mistakes. Thesis proposal --- Raluca Budiu

4. Memory for Text • People interpret new stories in terms of past experiences. • Doing that helps them remember the new stories better. • Doing than makes them deform the actual facts. Thesis proposal --- Raluca Budiu

5. Motivation Metaphors “Mistakes” Memory for text • Claim: all are facets of the same cognitive mechanism, which: • accounts for both fallibility and robustness • uses background knowledge as a heuristic in service of the current goal. Thesis proposal --- Raluca Budiu

6. At the semantic level, comprehension works bottom-up: all the information available is used to find an interpretation; top-down: the interpretation is further used to help comprehension or recall. Proof: a unique computational model in ACT-R (Anderson & Lebiere, 1998) explaining and unifying phenomena from various domains; satisfying a number of computational and empirical (i.e. fitting actual behavioral data) constraints. Thesis Topic: Comprehension Thesis proposal --- Raluca Budiu

7. Overview • Thesis topic; • A model for sentence comprehension; • Empirical constraints; • Computational constraints; • Summary and work plan. Thesis proposal --- Raluca Budiu

8. Words Semantic + Model thematic roles interpretation Background knowledge Noah take verb agent Ark prop place-oblique patient animals ark Semantic Interpretation Understanding a sentence =finding a matching interpretation/context in the background knowledge. Thesis proposal --- Raluca Budiu

9. take animals Noah ark Incremental From left to right omitting How Does the Model Work? How many did on the Farm context Farm context Ark context Ark context Ark context father raise Noah take verb verb agent agent Farm prop Ark prop place-oblique place-oblique patient patient animals animals ark farm Thesis proposal --- Raluca Budiu

10. = here the model may omit to check all the previous words Model in the Absence of Context Priming Read word Extract Word Meaning no yes Context? yes no Word matches context? Find context no Context found? no yes yes Old words match? Thesis proposal --- Raluca Budiu

11. Context Priming Different processing at the beginning and at the end of the sentence. ark? How many animals did Noah take on the Ark story 1. Boat or ship held to resemble that in which Noah and his family were preserved from the Deluge agent 2. A repository traditionally in or against the wall of a synagogue for the scrolls of the Torah Noah place-oblique patient verb animals ark(1) took Thesis proposal --- Raluca Budiu

12. = here the model may omit to check all the previous words Model With Context Priming Read word Extract Context Role Context role matches word? yes no Find context Sentence not comprehended Context found? no yes yes no no Old words match? Thesis proposal --- Raluca Budiu

13. Speak very briefly Distributed Meaning Assumption Bible char Navigator meaning meaning word Noah “Noah” meaning meaning Patriarch Married • Meaning retrieval = extracting word features; • Replace word meaning with feature as unit of processing; • Model remains the same. Thesis proposal --- Raluca Budiu

14. Summary of the Model • Incremental; • Trial-and-error strategy; • Mixture of bottom-upandtop-down strategies; • Incomplete processing (aka symbolic partial matching) • at the word meaning level (not all features extracted); • at the sentence level; • No syntactic processing: thematic roles are inputs. Thesis proposal --- Raluca Budiu

15. Overview • Thesis Topic; • Model; • Empirical constraints; • Computational constraints; • Summary and work plan. Thesis proposal --- Raluca Budiu

16. Metaphor-related Phenomena • Effects of position on metaphor understanding (Gerrig & Healy, 1983); • Effects of metaphoric truth on the judgement and recall of sentences of the type Some As are Bs (Glucksberg, Glidea & Bookin, 1982); • Interferences of literal and metaphoric truth on sentence judgements (Keysar, 1989); • Effects of context length on metaphor understanding (Ortony, Schallert, Reynolds & Antos, 1978); • Comprehension differences between different types of metaphors (Gibbs, 1990; Ortony et al. 1978; our data). Thesis proposal --- Raluca Budiu

17. Metaphor Position Effects Metaphor-first sentences take longer to comprehend than metaphor-second sentences(Gerrig & Healy, 1983). Drops of molten silver filled the sky 4.21s (4.23s) * Container context Container context Stars context The sky was filled with drops of molten silver 3.53s (2.84s) * Stars context Stars context * Predictions Thesis proposal --- Raluca Budiu

18. What Are Semantic Illusions? • How many animals of each kind did Moses take on the ark? • Semantic illusions are very robust (Reder & Kusbit, 1991); however, not anything can make an illusion. • Good vs. bad illusions: How many animals did Adam take on the ark? Thesis proposal --- Raluca Budiu

19. Semantic Illusion Datasets • Illusion rates for good and bad distortions (Ayers, Reder & Anderson, 1996); • Percent correct for good and bad distortions in the gist task (Ayers et al., 1996); • Latencies in the literal and gist task (Reder & Kusbit, 1991); • Processing of semantic anomalies and contradictions (Barton & Sanford, 1993); When an aircraft crashes, where should the survivors be buried? vs. When a bicycle accident occurs where should the survivors be buried? Thesis proposal --- Raluca Budiu

20. Good vs. Bad Illusions All levels of distortion are significantly different from one another. Thesis proposal --- Raluca Budiu

21. Modeling Semantic Illusions Moses • Model says “Distorted” if it finds no interpretation; • Key idea: meaning overlap (supported by van Oostendorp & Mul, 1990; van Oostendorp & Kok, 1990); • Model predicts an effect of position of distortion in the sentence: late distortions are harder to detect. Noah take Adam verb agent Ark prop place-oblique patient animals ark Thesis proposal --- Raluca Budiu

22. Memory for Text • Prior schemas can influence text memory (Bartlett, 1932; Bransford & Johnson, 1972; etc.); • If a text is consistent with a pre-existent script (paradigmatic situation/previous experience) • subjects recall more propositions from the text, • but also make more script-consistent intrusions (Owens, Bower & Black, 1979). Thesis proposal --- Raluca Budiu

23. Text Memory Datasets • Recall and recognition of sentences from multiple episodes related or not by a common setting (Owens et al., 1979); • Interferences from related stories on recall and recognition of text (Bower, Black & Turner, 1979); • Text recall in the presence or absence of a topic (Bransford & Johnson, 1972); • Recall of single, related and unrelated facts (Bradshaw and Anderson, 1982). Thesis proposal --- Raluca Budiu

24. Interferences Among Related Stories The number of intrusions can increase if subjects study more variants of the same script (Bower, Black & Turner, 1979): • At the Dentist’s --- about Bill • At the Doctor’s --- about Tom Thesis proposal --- Raluca Budiu

25. Story 1 (dentist’s) Story 2 (doctor’s) Modeling Script Effects Visiting-healthcare-professional script Script Propositions Studied Propositions Thesis proposal --- Raluca Budiu

26. Difficulties With Modeling Script Effects • Parsing the discourse into a unitary and coherent representation (solve the problem of binding); • Text representation that allows recursive schemas; • Modeling different types of intrusions, especially abstract intrusions: Studied Intruded • Bill paid the bill. Tom paid the bill. • The nurse x-rayed Bill’s The nurse checked Tom’s • teeth. blood pressure. Thesis proposal --- Raluca Budiu

27. Lexical Ambiguity Resolution • Although not designed for data from this domain, our model makes strong predictions about ambiguity resolution. • Does context influence meaning access for an ambiguous word? • Possible answer: both meanings are activated, but activation depends additively on both context and individual meaning frequency (Tabossi, 1988; Duffy, Morris & Rayner, 1988; Rayner & Duffy, 1986; Rayner & Frazier, 1989; Lucas, 1999). Thesis proposal --- Raluca Budiu

28. Lexical Ambiguity Datasets • Gaze duration on balanced and unbalanced homophones (Duffy et al., 1988); • Mean reading time per character in the disambiguation region (Duffy et al., 1988); Thesis proposal --- Raluca Budiu

29. Gaze Durations on Homophones Duffy et al. (1988) manipulated position of disambiguating region and relative frequency of the homophone’s meanings: • Disambiguating region before/after the homophone; • Homophone could be balanced (pitcher) or unbalanced (port); Thesis proposal --- Raluca Budiu

30. Gaze Duration on Homophones • Times longer than controls reflect multiple access. • Times equal with controls reflect selective access. Thesis proposal --- Raluca Budiu

31. mihaib: hide Time Spent on Disambiguating Region Thesis proposal --- Raluca Budiu

32. Fitting the Data • Disambiguation-after: • no context priming; • individual meaning activation is proportional with meaning frequency (ACT-R assumption); • ACT-R is serial (no multiple access), but close competitors can slow down retrieval (tentative ACT-R assumption). • Disambiguation-before: • context priming: context is an extra source of activation; • If the wrong meaning is more frequent, context priming may not be enough. Thesis proposal --- Raluca Budiu

33. Overview • Thesis Topic; • Model; • Empirical constraints: • Computational constraints; • Summary and work plan. Thesis proposal --- Raluca Budiu

34. Words Semantic + Model Thematic roles interpretation Background knowledge Computational Constraints • Realistic reaction times; • Integration with background knowledge; • Allowing for errors of the syntactic processor (i.e. wrong thematic roles). Thesis proposal --- Raluca Budiu

35. Words Semantic + Model Candidate thematic roles interpretation Background knowledge Syntactic Ambiguity As a Computational Constraint Garden path effects have been largely documented in the literature: • The horse raced past the barn fell; • The cop arrested by the detective was guilty of taking bribes. Solution: thematic roles as meaning features later omitted. Thesis proposal --- Raluca Budiu

36. Summary • Language comprehension theory to be embodied in a unique ACT-R model; • Semantic rather than syntactic level of processing (no parser); • The theory should satisfy: • Computational constraints: • Realistic reaction times; • Integration with background knowledge; • Syntactic ambiguity. • Empirical constraints • Metaphor understanding; • Semantic illusions; • Lexical ambiguity; • Memory for text: script effects and elaborations. Thesis proposal --- Raluca Budiu

37. Empirical Constraints Metaphor understanding: • Effects of position on metaphor understanding (Gerrig & Healy, 1983); • Effects of metaphoric truth on the judgement and recall of sentences of the type Some As are Bs (Glucksberg et al., 1982); • Interferences of literal and metaphoric truth on sentence judgements (Keysar, 1989); • Effects of context length on metaphor understanding (Ortony et al., 1978); • Comprehension differences between different types of metaphors (Gibbs, 1990; Ortony et al. 1979; our data). Thesis proposal --- Raluca Budiu

38. Empirical Constraints (contd.) • Semantic illusions: • Illusion rates for good and bad distortions in the literal and gist tasks (Ayers et al., 1996); • Latencies in the literal and gist task (Reder & Kusbit, 1991); • Processing of semantic anomalies and contradictions (Barton & Sanford, 1993). • Lexical ambiguity: • Gaze duration on balanced and unbalanced homophones (Duffy et al., 1988); • Mean reading time per character in the disambiguation region (Duffy et al., 1988); Thesis proposal --- Raluca Budiu

39. Empirical Constraints (contd.) • Memory for text (script effects and elaborations): • Recall and recognition of sentences from multiple episodes related or not by a common setting (Owens et al., 1979); • Interferences from related stories on recall and recognition of text (Bower et al., 1979); • Text recall in the presence or absence of a topic (Bransford & Johnson, 1972); • Recall of single, related and unrelated facts (Bradshaw and Anderson, 1982). Thesis proposal --- Raluca Budiu

40. Model Validation • Collect new empirical data to validate “side effects” or other predictions of the model, not covered by the previous list of empirical phenomena: E.g.: position effects for Moses’ illusion. • Test it on other sets of data (for the same phenomena) than the ones it has been built for in order to avoid “overfitting”. Thesis proposal --- Raluca Budiu

41. Work Plan • Modeling and parameter fitting; • Datacollection: metaphors and semantic illusions; • The model still has to solve the more difficult problems of discourse representation. Garden path Metaphor Lexical ambiguity Semantic illusions Text memory 20% 30% 15% 10% 25% Thesis proposal --- Raluca Budiu