1. Together at Last: How Tense and Aspect Interact in Simulation Semantics Nancy Chang Laura A. Michaelis Srinivas Narayanan Department of Computer Science/International Computer Science Institute UC Berkeley nchang@icsi.berkeley.edu Department of Linguistics/ Institute of Cognitive Science University of Colorado at Boulder michaelis@colorado.edu Department of Computer Science/International Computer Science Institute UC Berkeley narayan@icsi.berkeley.edu

2. The Problem  Aspectual theorists have proposed revealing models of type shifts, both explicit and implicit: Explicit type-shifting Explicit type-shifting Progressive: Process  State (e.g., She was singing)Progressive: Process  State (e.g., She was singing) Perfect: Event  State (e.g., The Eagle has landed)Perfect: Event  State (e.g., The Eagle has landed) Implicit type-shifting (coercion) Implicit type-shifting (coercion) State  episode of stasis: She was depressed twice.State  episode of stasis: She was depressed twice. State  inchoative event: I liked her within a minute.State  inchoative event: I liked her within a minute.  These models rely on an ontology of idealized situation types (process, state, event, etc.).  In this respect, they are incompatible with models of tense.

3. The Problem  Unlike aspect, tense lacks an ontology.  Tense is purely relational: there are no ‘tense-type entities’ corresponding to aspectual-type entities.  Tense relates speech time to reference time (R).  R is located relative to the encoded situation; it constrains inference potential.  Tense semantics are therefore based on a location metaphor: R is speaker/hearer location.  By contrast, aspectual semantics are based on an entity metaphor.  But tense and aspect are much more similar than the traditional models imply.

4. Tense-Aspect Interfaces  Tenses convey aspectual perspectives like ‘attention to endpoints’: My ex-husband is/was Latvian. My ex-husband is/was Latvian. I took a cab over. The driver was/?is Latvian. I took a cab over. The driver was/?is Latvian.  Aspect determines ‘direction of inclusion’ for R: State: Pat was in Cleveland in June. (state overflows R) State: Pat was in Cleveland in June. (state overflows R) Event: Pat visited Cleveland in June. (event exhausted by R) Event: Pat visited Cleveland in June. (event exhausted by R)  Tense and aspect jointly encode overlap/sequence: I told them I had (had) more time on the meter. I told them I had (had) more time on the meter.  Tenses select aspectual types, and as such are coercion triggers. a eu Margot s’est retournée. Henri avait l’air heureux. Margot s’est retournée. Henri avait l’air heureux. ‘Marge turned around. Harry was (perf/imperf) happy.’

5. Our Purpose  We will propose a simulation-based model of tense that is isomorphic to models of phasal aspect proposed by Narayanan 1997 and Chang, Gildea & Narayanan 2000.  This model will capture tense-aspect interfaces, in particular the role of tense in triggering aspectual type- shifts.  We will use this model to account for two traditionally mysterious kinds of semantic effects: Habitual coercion: She took the bus to work that summer. Habitual coercion: She took the bus to work that summer. Present-tense coercion: She takes the bus to work. Present-tense coercion: She takes the bus to work.  We will model these type shifts by assuming x-schemas for tense that are combinable with aspectual x-schemas.  Tense x-schemas will be known as sampling sets.

6. Coercion by Tense: Habituals  Habitual predications count as state predications: Extensibility Extensibility She smoked back then and I think she still does.She smoked back then and I think she still does. *She enlisted in 1985 and I think she still does.*She enlisted in 1985 and I think she still does. Present-tense reporting Present-tense reporting She smokes.She smokes. *Look! That woman smokes a cigar.*Look! That woman smokes a cigar.  However, via internal composition they are not states but iterated events (like pacing, bouncing a ball, etc.).  No predication is born habitual, so in the absence of a dedicated habitual marker, habitual meaning must come from an implicit type shift.  But what grammatical form performs this type shift?

7. Coercion by Tense: Present  The present tense is a state selector (Langacker 1987): *She is ill for three days. *She is ill for three days. *She dribbles the basketball. *She dribbles the basketball.  Present distorts Aktionsart projected by predicators: Futures from perfective stem+present inflection, e.g., Latin amabo ‘I will love’ (<amav + o) Futures from perfective stem+present inflection, e.g., Latin amabo ‘I will love’ (<amav + o) Habitual-event reports: Habitual-event reports: She visits her mother. (possible iff assigned a habitual reading)She visits her mother. (possible iff assigned a habitual reading) ‘Progressive’ reports in, e.g., French: ‘Progressive’ reports in, e.g., French: Eh bien, à present, je me sens mieux. Le morale revient. ‘Now I feel better. My morale is coming back.’ (Binet, Bidochon 8: 42)Eh bien, à present, je me sens mieux. Le morale revient. ‘Now I feel better. My morale is coming back.’ (Binet, Bidochon 8: 42) ‘Perfect’ reports in, e.g., French: ‘Perfect’ reports in, e.g., French: Ca fait dix minutes qu’elle nous parle de la moquette! ‘She’s been telling us about the carpet for 10 minutes.’ (Binet, Bidochon 10:17)Ca fait dix minutes qu’elle nous parle de la moquette! ‘She’s been telling us about the carpet for 10 minutes.’ (Binet, Bidochon 10:17)

8. Simulation Semantics  Inspired by biological control theory, Simulation Semantics models events as executing-, or x-schemas.  An x-schema is a Petri net: a weighted graph consisting of places (circles) and transitions (rectangles) connected by directed input and output arcs.  A state is defined by the placement of a token (a black dot or number) in a particular place.  The real-time execution semantics of Petri nets models the production and consumption of resources: A transition is enabled when its input places are marked such that it can fire by movement of tokens from input to output. A transition is enabled when its input places are marked such that it can fire by movement of tokens from input to output. Arcs include resource, enable and inhibitory arcs. Arcs include resource, enable and inhibitory arcs. Actions have hierarchical structure, permitting embeddings. Actions have hierarchical structure, permitting embeddings.

9. Basic X-Schema Distinctions State  Obtains (if marked).  Momentaneous simulation/verification Transition  Fires to simulate an event Change of State  Transition entails pre- and post-states.  Firing removes tokens from pre-state(s) and produces tokens on the post-state(s)

10. X-Schemas and Aktionsart

11. A Schema Controller The controller sends signals to the embedded schema. It transitions based on signals from the embedded schema. It captures higher level coordination of actions. Ready DoneStartProcessFinish Suspend Cancel interrupt resume iterate

12. Phasal Aspects Map to Controller Ready DoneStartProcessFinish Suspend Cancel interruptresume Iterate Inceptive (start, begin) Iterative (repeat) Completive (finish, end)Resumptive(resume)

13. Phases of Climb Ready DoneStartProcessFinish Suspend Cancel interrupt resume Iterate Energy Ready Standing On top Hold Find hold Pull(self) Stabilize BINDINGS

14. About to Climb (Prospective) Ready DoneStartProcessFinish Suspend Cancel interrupt resume Iterate Energy Ready Standing On top Hold Find hold Pull(self) Stabilize BINDINGS

15. Be- Climbing (Progressive) Ready DoneStartProcessFinish Suspend Cancel interrupt resume Iterate Energy Ready Standing On top Hold Find hold Pull(self) Stabilize BINDINGS

16. Have- Climbed (Perfect) Energy Ready Standing On top Hold Find hold Pull(self) Stabilize Ready DoneStartProcessFinish Suspend Cancel interrupt resume Iterate BINDINGS

17. Tense and Reference Time  Tense is defined by the location of reference time relative to speech time in the traditional (Reichenbach- based) view: Past tense = E,R < S Past tense = E,R < S Present tense = E,R,S Present tense = E,R,S  In Simulation Semantics, R is a state of the controller schema.  The location of R relative to S is captured by a time- depth representation called a sampling set: Present = single state Present = single state Past = two states (onset and offset) Past = two states (onset and offset) The final or unique state is the state of knowledge at S. The final or unique state is the state of knowledge at S.

18. Aspect and Reference Time  In Simulation Semantics, ‘direction of inclusion’ is represented by the presence or absence of a transition between pre- and post-states in the sampling set.  In the case of the present tense, there is only one state in the sampling set.  Only states can be verified on the basis of a single temporal sample.  Therefore, present-tense reports are state reports.

19. Aspect and Reference Time  The sampling set for the past tense may include a transition between the two states.  The presence or absence of this transition captures the difference between perfective and imperfective past: Perfective: Pat was in Cleveland yesterday for the trade show. (yesterday includes offset transition) Perfective: Pat was in Cleveland yesterday for the trade show. (yesterday includes offset transition) Imperfective: Pat was in Cleveland yesterday, and in fact has been there all month. (yesterday does not include an offset transition) Imperfective: Pat was in Cleveland yesterday, and in fact has been there all month. (yesterday does not include an offset transition)  The perfective and imperfective schemas are not alternate event types, nor are they alternate construals of a single situation; they are sets of sets of places within the controller schema.

20. Aspectually Sensitive Past Tenses Perfective Imperfective

21. Aspectually Sensitive Past Tenses Perfective Imperfective

22. Type-Shifting Revisited  In Simulation Semantics, a stative type shift is selection of a state of the controller.  The combination of controller and base x-schema can (a) augment, (b) embed or (c) select a part of the base x- schema: Augmentation: State  inchoative state, e.g., Suddenly, I knew the answer. Augmentation: State  inchoative state, e.g., Suddenly, I knew the answer. Embedding: Event  iterated event  habitual state, e.g., She runs. Embedding: Event  iterated event  habitual state, e.g., She runs. Selection: Inchoative  state, e.g., Your soup is cooled. Selection: Inchoative  state, e.g., Your soup is cooled.  Additional examples: present-tense coercion, progressive type-shifting, continuative perfect type- shifting.

23. Habituality  Habitual situations, e.g., Jan ran, count as events, just like iterated actions, e.g., Jan paced back and forth.  Habituals are therefore not intrinsically stative; they are events embedded in an iterate schema: iterate

24. Stative Coercion  Habituals become stative solely by virtue of being embedded in the stative process schema.  This schema can be selected by the present tense. ongoing present

25. Conclusion  In Simulation Semantics, tense and aspect are represented in a uniform manner.  Rather than Aktionsart plus a time line and perspective, we have combination of simulations.  Combinations model iteration, the selection of subparts and augmentation of schemas.  Using these mechanisms, we account for tense-based coercions, e.g., stative coercion via present tense.  How do we build ECG representations for tense inflections that abstract over the various morphological instantiations of tense (affixation, suppletion, etc.)?  Can we assume that English, like Romance, has both state- and event-selecting past tenses?