Lessons from an Artificial Intelligence Research Project on Metaphor

1. Lessons froman Artificial Intelligence Research Project on Metaphor John Barnden School of Computer Science University of Birmingham, UK Collaborators: Sheila Glasbey, Mark Lee, Alan Wallington

2. Plan of Talk • Some general observations • The ATT-meta approach and system • Lessons regarding language differences/learning

3. What I Take Metaphor to Be • Talking/thinking/… about something (the target) as if it were something else (the source). • Taking a “Metaphorical view.” • Some examples: • My work will spill over into the weekend. • Thatcher was the Reagan of Britain. • Managerialism is creeping into academia. • Metaphorical views involve mappings.

4. Cross-Linguistic/Cross-Cultural Issues • Many metaphorical views are common across languages. • e.g. Problem/Issue As Physical Object • special case: Major Issue As Iceberg [King 2004] • But some aren’t: • e.g. [Papafragou 1998]: • Idea as Edible Object not allowed in Greek. • [Littlemore & Low, forthcoming]: • English hat metaphors don’t translate well into French • And even when they are, word-for-word translation often does not give good results [King 2004]. • drop of water on a hot stone [from German phrase] • behind the tip of the iceberg; visible sides of the iceberg [from Greek phrases]

5. Types of Metaphoricity in Language • When the utterance uses familiar metaphorical views • AND the source aspects used are mapped by them • AND the wording is standard: • One part of me thought that I should go. • When the utterance uses familiar metaphorical views • AND the source aspects used are mapped by them • BUT the wording is not standard: • One component of me thought that I should go.

6. MAP TRANSCENDING: • When the utterance uses familiar metaphorical views BUT some source aspect is NOTmapped by them: • One part of me wasinsisting that I should go. • SnakeByte technologies gobbled up RabbitWare and spat its managers out.

7. NOVEL: • When the utterance does not use familiar views: • John unpeeled the temperature. • The road was a dolphin.

8. Map-Transcending Metaphor, contd. • He dredged up his mud-encrusted memories. • In the far reaches ofher mind, Anne knew that … • Men aren’t islands, but some are peninsulas. • The middle managers have cricks in their necks from talking down to the workers and up to the bosses.

9. Language-User Relativity(some links toCameron 1999, Geeraerts 2002, Ruiz de Mendoza Ibáñez 1999, Radden 2002, Radman 1997, Riemer 2002) • Relativity of familiarity of views / mappings • Relativity of lexical senses and standardness of wording • Hence, relativity of: • whether an utterance is actively metaphorical (i.e. requires source/target mapping actions) • what metaphorical views an utterance rests on • whether an utterance is map-transcending, novel, etc.

10. Domains in Metaphor: Problems(cf. Barcelona 2002, Cameron 1999, Kittay 1989, Lemmens 2001, Riemer 2002) • Domain divisions are context-sensitive and arbitrary: • “Peter is a fox” • “The idea lurked in his mind” • “Thatcher was the British Reagan” • “Christmas is on the horizon” • “The idea had sunk slowly into his subconscious.”

11. Domains in Metaphor: contd • Source and target domains can massively overlap: • e.g. -- Mind Parts As Persons: • “One part of me says I should go to the party, another part insists I should do my tax form.” • Other types of overlap/arbitrariness – where should the following facts go?? • Minds are not containers. • Cars can be the setting for passionate love.

12. View-Neutral Mapping Adjuncts[cf. Carbonell, (Winston)] • Emotions, value judgmentsandmental statesare often implicitly transferred from source to target in metaphor in general. (May even be a primary function of metaphor.) • Managerialism is sneaking into academia. • Poverty is a disease. • We’re conducting a war on terrorism. • The emotions, etc. can be of agents in the source, rather than of the understander.

13. Other VNMAs • uncertainty • degrees (intensities) • causation, enablement, ability, ease, etc. • event shape, temporal relationships • sets, qualitative set sizes.

14. VNMAs, contd “John and Mary are in a race with each other at work.” • Rests on Abstract Process as Physical Journey. • John & Mary are viewed as being in a race. • So each intendsto win that race, i.e. to finish the race journey first. • The finishingand thefirst-ness map by VNMAs. • So each intendsto finish their work first, by another VNMA.

15. ATT-Meta Approach • Reasoning approach for metaphor processing. • Suited to linguistic & non-linguistic metaphor. • Aimed mainly at map-transcending metaphor. Does not discover mappings. • Exploits view-neutral mapping adjuncts.

16. Emphasizes degrees (gradations). • Emphasizes qualitative uncertainty. • Allows source information to override target. • Allows combinations of metaphorical views. • (Parallel & serial.)

17. ATT-Meta System • (Partially) implements the approach. • Rule-based. • Much attention to uncertainty handling. • Metaphor-orientated reasoning thoroughly integrated into overall reasoning. • Metaphor override phenomena absorbed into general conflict-resolution approach.

18. Basic Method in ATT-Meta[cf. Carbonell, Hobbs, Narayanan, (Lakoff, Martin)] EXAMPLE: “In the far reaches of her mind, Anne knew that Kyle was having an affair.” [Cosmopolitan, 1994] Mind as Physical Space, Ideas as Physical Objects (implicitly) System knows VIEW-SPECIFIC MAPPING: physical manipulation of ideas conscious mental usage

19. PRETEND that the utterance’s source-domain meaning is true. • REASON (VIA SEVERAL STEPS) within a special computational pretence environment that, • presumably, Anne canphysically operate upon the Kyle-affair idea only to a very low degree. • Apply VIEW-SPECIFIC MAPPING & VNMAs: • presumably, Anne can consciously mentally use the Kyle-affair idea only to a very low degree.

20. Another Example • “A part of Mary was insisting that she was right.” • Mind Parts as Persons: a part believes X  whole agent has motivation to believe X • REASONING in the PRETENCE: • The mentioned part believes that Mary is right. • There is another part that hasstated that Mary is not right. • That other part believes that Mary is not right. • Result of KNOWN MAPPING: • Mary has a motivation to believe she is right. • Mary has a motivation to believe she is not right.

21. Map-Extension Minimization • Metaphor understanding should try to avoid creating new mapping relationships for: • map-transcending aspects of the utterance • “far reaches,” “mud-encrusted”, “neck cricks,” “peninsulas,” “insisting,” … • general source-domain knowledge exploited • Much within-pretence knowledge and reasoning serves merely to facilitate and warrant the application of already-known mapping relationships (view-specific & VNMAs).

22. Where Mappings Go • They go from pretence environments to surrounding environments (usually reality) … NOT from source domains to target domains. • If we’re pretending that an idea is a physical object, and that X physically manipulates it, • then (in reality)X is consciously using it. • This stance sidesteps the problems with domains.

23. Context-Sensitivity(Leezenberg 1995, Stern 2000, …) “Peter’s a tank.” What might this convey?? KEY: The example is only likely to arise in an already established context.

24. Context-Drivenness “Peter’s colleagues are badly affected by criticism, but he’s a tank.” • First clause and the “but” raise issue of how badly affected PETER is by criticism. • Suppose we know a metaphorical mapping from physical attack in a battle to criticism. • Then, in a metaphorical pretence, raise issue of how badly affected Peter-as-tank is by physical attack. • Address the issue by knowledge about tanks. • NB: Backwards use of metaphorical mapping.

25. Context-Drivenness in ATT-Meta • In ATT-Meta, reasoning is directed “backwards” from goals (a standard technique in AI). • Enables metaphor understanding to be context-driven. • “In the far reaches of her mind, Anne knew Kyle was having an affair, butto acknowledge the betrayal to herself would have meant she would have had to take a stand.” • “acknowledge to herself” and the “but” raise the issue of Anne’s conscious awareness. Mapped backwards to within-pretence issue of physical manipulability.

26. Lessons (Suggestions) concerning Language Differences, Learning, etc. • Emphasis on • within-pretence reasoning • view-neutral mapping adjuncts, and hence • small number of view-specific mappings per view, • allows: • Relatively little learning of view-specific mappings in L2

27. Some issues raised: • To what extent are view-neutral mapping adjuncts (VNMAs) universal? • (Many discussions assume that, e.g., value judgments are carried over in any language.) • We need to attend to different types of value, emotion, mental state, event conceptions, etc. in different languages/cultures.

28. Littlemore & Low (forthcoming) stress connections between metaphoric thinking and learning L2-culture-specific within-source connotations of words/concepts. • “Silence is golden.” • Our emphasis on VNMAs (if largely universal) and on avoiding new mappings allows: • Focussing on specific sorts of connotation: those that connect to known mappings

29. Language-user relativity even within a given language/culture means: • There is less pressure on teachers/learners to attend to culture-specific metaphorical language, mappings and domain divisions as opposed to • culture-specific NON-metaphorical connotations (golden  valuable, good) • and • general principles of metaphoric processing.

30. Our focus on uncertainty reveals that metaphor-derived information often overrides target defaults. “The company nursed its competitor back to health.” (Conjecture: This is a major function of metaphor.) • Metaphor is therefore even more important in language learning than it would be otherwise.

31. An emphasis on context means that: • Teachers/learners can rest assured that usually context will help a lot in understanding metaphor (especially when unconventional). • Best not to base metaphor-related learning on isolated sentences. • Caution: students may not use the right part of the context [Littlemore]

32. Conclusions • Importance of map-transcendence in mundane uses of metaphor. • Treatment by a reasoning-heavy approach without (in general) creation of new mappings. • Nice fit with a pretence view of metaphor, which also sidesteps domain problems. • Importance of relativity, uncertainty, degrees, context, and view-neutral mapping principles. • Some suggestions regarding cross-culture/cross-language differences/learning/teaching. • Heightening of the problem of the metaphor/metonymy distinction.

33. Variation of Metaphorical Idiom[cf. Moon 1998] • “in the recesses of X’s mind” could be in a lexicon, a WordNet, etc. • But productive variation is possible: • in the dim recesses of X’s mind • in the deep recesses of X’s mind • in the distant recesses of X’s mind • in the unlit recesses of X’s mind • Such variation is often map-transcending.

34. Other Practical Features of Metaphor • Metaphor as exception-handler. [My conjecture.] • She knew it in the dark recesses of her mind. • Copular metaphor (A is B) as summarizer [Kupferberg & Green 1998, Drew & Holt 1995]. • Mary’s a real bulldozer. Do you know what she did … • Metaphorical idioms/proverbs as topic pivots [Drew & Holt 1995]. • Yep, too many cooks spoil the broth. Now what I wanted to say was … • Meta-discourse metaphor [Cameron 1998]. • Let’s circle back now to the first principle I mentioned.

35. Lessons from Being Computational • Context-drivenness. • Grappling with the problems with domains. • Settling on pretence/reality distinction as opposed to source/target domain distinction. • Importance of uncertainty and reasoning-conflict resolution. • Metaphorical inference can defeat target defaults. • Importance of degrees. • Need for view-neutral mapping adjuncts.

36. Cross-Linguistic/Cultural Issues, contd. • Productivity of metaphor. • Example: could learn a translation for the phrase “race condition” in distributed computing: but then words such as “win,” “defeat,” “tie,” “drop out” etc. etc. need to be treated systematically.

37. No need to have view-specific mapping that deals with degrees of , ability etc. of physical manipulation of ideas. • Contrast with a Lakoff conceptual metaphor: • ((FILL IN))

38. The Method … Not! • No necessary reliance on LITERAL meaning. A source-domain meaning is not necessarily “literal”. • But even when source-domain meaning can be said to be literal, the method is NOT “literal-first.”