1. Spatial and Planning Models of ASL Classifier Predicates for Machine Translation Matt Huenerfauth 10 th International Conference on Theoretical and Methodological Issues in Machine Translation October 4, 2004 Baltimore, MD, USA Computer and Information Science University of Pennsylvania Research Advisors: Mitch Marcus & Martha Palmer

2. Motivations and Applications Only half of Deaf high school graduates (age 18+) can read English at a fourth-grade (age 10) level, despite ASL fluency. Many Deaf accessibility tools forget that English is a second language for these students (and has a different structure). Applications for a Machine Translation System: –TV captioning, teletype telephones. –Computer user-interfaces in ASL. –Educational tools, access to information/media. –Transcription, storage, and transmission of ASL.

3. Input / Output What’s our input? English Text. What’s our output? ASL has no written form. Imagine a 3D virtual reality human being… One that can perform sign language… What’s our input? English Text. What’s our output? ASL has no written form. Imagine a 3D virtual reality human being… One that can perform sign language… But this character needs a set of instructions telling it how to move! Our job: English  These Instructions. VCom3d

4. Photos: Seamless Solutions, Inc. Simon the Signer (Bangham et al. “Signing for the Deaf Using Virtual Humans,” IEE2000.) Vcom3D Corporation Off-the-Shelf Virtual Humans

5. ASL Linguistics Some ASL sentences: structure similar to that of spoken/written languages. Other ASL sentences: use space around signer to topologically describe the 3D layout of a scene under discussion. –The hands indicate the movement and location of entities in the scene. –Called “Classifier Predicates.”

6. Classifier Predicate Gaze Right Left Gaze Right Left The car parked between the cat and the house. Viewer sign: HOUSE Viewer sign: CAT Viewer sign: CAR Note: Facial expression, head tilt, and shoulder tilt not included in this example. Loc#3 To Loc#3 Loc#1 To Loc#1 Eyes follow right hand. Path of car, stop at Loc#2. To Loc#2 Example (Loc#2) (Loc#3) (Loc#1)

7. Previous ASL MT Systems Little ASL corpora – no statistical systems. Previous direct and transfer systems are only partial solutions. –Some produce only Signed English, not ASL. –None handle the spatial aspects of ASL. All ignore classifier predicates.

8. We can’t ignore CPs CPs are needed to convey many concepts. Signers use CPs frequently. * CPs needed for some important applications –ASL user-interfaces –literacy educational software * Morford and McFarland. 2003. “Sign Frequency Characteristics of ASL.” Sign Language Studies. 3:2.

9. Focus and Assumptions Focus of this approach: producing classifier predicates of movement and location. Part of a larger project * to develop a multi-path English-ASL MT architecture –Direct/transfer paths: most sentences. –This path: produce Classifier Predicates. * Huenerfauth, M. 2004. “A Multi-Path Architecture for English-to-ASL MT.” HLT-NAACL Student Workshop.

10. ASL Classifier Predicate Models

11. Overall Architecture English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event CP Semantics CP Syntax CP Phonology CP Discourse

12. CP Translation Models Discussed Scene Visualization Discourse Semantics Syntax Phonology (we’ll talk about this one first)

13. Phonological Model Body Parts Moving Through Space: “Articulators” English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event CP Semantics CP Syntax CP Phonology CP Discourse Overall Architecture

14. ASL Phonetics/Phonology “Phonetic” Representation of Output –Hundreds of animation joint angles. Traditional ASL Phonological Models –Hand: shape, orientation, location, movement –Some specification of non-manual features. –Tailored to non-CP output: Difficult to specify complex motion paths. CPs don’t use as many handshapes and orientation patterns.

15. Classifier Predicate Gaze Right Left Gaze Right Left The car parked between the cat and the house. At Viewer sign: HOUSE At Viewer sign: CAT At Viewer sign: CAR Note: Facial expression, head tilt, and shoulder tilt not included in this example. Location #3 To Loc #3 Location #1 To Loc #1 Eyes follow right hand. Path of car, stop at Loc #2. To Location #2 Example

16. Phonological Model What is the output? –Abstract model of (somewhat) independent body parts. “Articulators” –Dominant Hand (Right) –Non-Dominant Hand (Left) –Eye Gaze –Head Tilt –Shoulder Tilt –Facial Expression What information do we specify for each of these?

17. Values for Articulators Dominant Hand, Non-Dominant Hand –3D point in space in front of the signer –Palm orientation –Hand shape (finite set of standard shapes) Eye Gaze, Head Tilt –3D point in space at which they are aimed.

18. English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event Scene Visualization Approach Converting an English sentence into a 3D animation of an event. CP Semantics CP Syntax CP Phonology CP Discourse Overall Architecture

19. Previously-Built Technology AnimNL System –Virtual reality model of 3D scene. –Input: English sentences that tell the characters/objects in the scene what to do. –Output: An animation in which the characters/objects obey the English commands. Bindiganavale, Schuler, Allbeck, Badler, Joshi, & Palmer. 2000. "Dynamically Altering Agent Behaviors Using Nat. Lang. Instructions." Int'l Conf. on Autonomous Agents. Related Work: Coyne and Sproat. 2001. “WordsEye: An Automatic Text-to-Scene Conversion System.” SIGGRAPH-2001. Los Angeles, CA.

20. English Sentence Pred-Arg Structure 3D Animation of the Event How It Works 3D Animation Planning Operator We won’t discuss all the details, but one part of the process is important to understand. (We’ll come back to it later.)

21. Step 1: Analyzing English Input The car parked between the cat and the house. Syntactic analysis. Identify word senses: e.g. park-23 Identify discourse entities: car, cat, house. Predicate Argument Structure –Predicate: park-23 –Agent: the car –Location: between the cat and the house Example

22. Step 2: AnimNL builds 3D scene Example

23. CP Discourse CP Semantics CP Syntax CP Phonology English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event Discourse Model Overall Architecture

24. Discourse Model Motivations Preconditions for Performing a CP –(Entity is the current topic) OR (Starting point of this CP is the same as the ending point of a previous CP) Effect of a CP Performance –(Entity is topicalized) AND (assigned a 3D location) Discourse Model must record: –topicalized status of each entity –whether a point has been assigned to an entity –whether entity has moved in the virtual reality since the last time the signer showed its location with a CP

25. Discourse Model Topic(x) – X is the current topic. Identify(x) – X has been associated with a location in space. Position(x) – X has not moved since the last time that it was placed using a CP.

26. Step 3: Setting up Discourse Model Model includes a subset of the entities in the 3D scene: those mentioned in the text. All values initially set to false for each entity. CAR: __ Topic? __ Location Identified? __ Still in Same Position? HOUSE: __ Topic? __ Location Identified? __ Still in Same Position? CAT: __ Topic? __ Location Identified? __ Still in Same Position? Example

27. CP Semantics Semantic Model Invisible 3D Placeholders: “Ghosts” CP Discourse CP Syntax CP Phonology English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event Overall Architecture

28. Semantic Model 3D representation of the arrangement of invisible placeholder objects in space These “ghosts” will be positioned based on the 3D virtual reality scene coordinates Choose the details, viewpoint, and timescale of the virtual reality scene for use by CPs

29. Step 4: Producing Ghost Scene Example HOUSE CAR CAT

30. CP Syntax Syntactic Model Planning-Based Generation of CPs CP Discourse CP Semantics CP Phonology English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event Overall Architecture

31. CP Templates Recent linguistic analyses of CPs suggests that they can be generated by: –Storing a lexicon of CP templates. –Selecting a template that expresses the proper semantics and/or shows proper 3D movement. –Instantiate the template by filling in the relevant 3D locations in space. Huenerfauth, M. 2004. “Spatial Representation of Classifier Predicates for MT into ASL.” Workshop on Representation and Processing of Signed Languages, LREC-2004. Liddel, S. 2003. Grammar, Gesture, and Meaning in ASL. Cambridge University Press.

32. Animation Planning Process This mechanism is actually analogous to how the AnimNL system generates 3D virtual reality scenes from English text. –Stores templates of prototypical animation movements (as hierarchical planning operators) –Select a template based on English semantics –Use planning process to work out preconditions and effects to produce a 3D animation of event

33. Example Database of Templates WALKING-UPRIGHT-FIGURE Parameters: g0 (ghost car parking), g1..gN (other ghosts) Restrictions: g0 is a vehicle Preconditions: topic(g0) or (ident(g0) and positioned(g0)) for g=g1..gN: (ident(g) and positioned(g)) Articulator: Right Hand Location: Follow_location_of( g0 ) Orientation: Direction_of_motion_path( g0 ) Handshape: “Sideways 3” Effects: positioned(g0), topic(g0), express (park-23 ag:g0 loc:g1..gN ) Concurrently: PLATFORM(g0.loc.final), EYETRACK(g0) MOVING-MOTORIZED-VEHICLE Parameters: g0 (ghost car parking), g1..gN (other ghosts) Restrictions: g0 is a vehicle Preconditions: topic(g0) or (ident(g0) and positioned(g0)) for g=g1..gN: (ident(g) and positioned(g)) Articulator: Right Hand Location: Follow_location_of( g0 ) Orientation: Direction_of_motion_path( g0 ) Handshape: “Sideways 3” Effects: positioned(g0), topic(g0), express (park-23 ag:g0 loc:g1..gN ) Concurrently: PLATFORM(g0.loc.final), EYETRACK(g0) LOCATE-BULKY-OBJECT Parameters: g0 (ghost car parking), g1..gN (other ghosts) Restrictions: g0 is a vehicle Preconditions: topic(g0) or (ident(g0) and positioned(g0)) for g=g1..gN: (ident(g) and positioned(g)) Articulator: Right Hand Location: Follow_location_of( g0 ) Orientation: Direction_of_motion_path( g0 ) Handshape: “Sideways 3” Effects: positioned(g0), topic(g0), express (park-23 ag:g0 loc:g1..gN ) Concurrently: PLATFORM(g0.loc.final), EYETRACK(g0) TWO-APPROACHING-UPRIGHT-FIGURES Parameters: g0 (ghost car parking), g1..gN (other ghosts) Restrictions: g0 is a vehicle Preconditions: topic(g0) or (ident(g0) and positioned(g0)) for g=g1..gN: (ident(g) and positioned(g)) Articulator: Right Hand Location: Follow_location_of( g0 ) Orientation: Direction_of_motion_path( g0 ) Handshape: “Sideways 3” Effects: positioned(g0), topic(g0), express (park-23 ag:g0 loc:g1..gN ) Concurrently: PLATFORM(g0.loc.final), EYETRACK(g0) LOCATE-SEATED-HUMAN Parameters: g0 (ghost car parking), g1..gN (other ghosts) Restrictions: g0 is a vehicle Preconditions: topic(g0) or (ident(g0) and positioned(g0)) for g=g1..gN: (ident(g) and positioned(g)) Articulator: Right Hand Location: Follow_location_of( g0 ) Orientation: Direction_of_motion_path( g0 ) Handshape: “Sideways 3” Effects: positioned(g0), topic(g0), express (park-23 ag:g0 loc:g1..gN ) Concurrently: PLATFORM(g0.loc.final), EYETRACK(g0) PARKING-VEHICLE Parameters: g0 (ghost car parking), g1..gN (other ghosts) Restrictions: g0 is a vehicle Preconditions: topic(g0) or (ident(g0) and position (g0)) for g=g1..gN: (ident(g) and position (g)) Articulator: Right Hand Location: Follow_location_of( g0 ) Orientation: Direction_of_motion_path( g0 ) Handshape: “Sideways 3” Effects: positioned(g0), topic(g0), express (park-23 ag:g0 loc:g1..gN ) Concurrently: PLATFORM(g0.loc.final), EYETRACK(g0)

34. Step 5: Initial Planner Goal Planning starts with a “goal.” Express the semantics of the sentence: –Predicate: PARK-23 –Agent: “the car” discourse entity We know from lexical information that this “car” is a vehicle (some special CPs may apply) –Location: 3D position calculated “between” locations for “the cat” and “the house.” Example

35. Step 6: Select Initial CP Template PARKING-VEHICLE Parameters: g_0, g_1, g_2 (ghost car & nearby objects) Restrictions: g_0 is a vehicle Preconditions: topic( g_0 ) or ( ident( g_0 ) and position( g_0 )) (ident( g_1 ) and position( g_1 )) (ident( g_2 ) and position( g_2 )) Articulator: Right Hand Location: Follow_location_of( g_0 ) Orientation: Direction_of_motion_path( g_0 ) Handshape: “Sideways 3” Effects: position( g_0 ), topic( g_0 ), express(park-23 agt: g_0 loc: g_1, g_2 ) Concurrently: PLATFORM( g_0.loc.final), EYETRACK( g_0 ) Example

36. Step 7: Instantiate the Template PARKING-VEHICLE Parameters: CAR, HOUSE, CAT Restrictions: CAR is a vehicle Preconditions: topic(CAR) or (ident(CAR) and position(CAR)) (ident(CAT) and position(CAT)) (ident(HOUSE) and position(HOUSE)) Articulator: Right Hand Location: Follow_location_of( CAR ) Orientation: Direction_of_motion_path( CAR ) Handshape: “Sideways 3” Effects: position(CAR), topic(CAR), express(park-23 agt:CAR loc:HOUSE,CAT ) Concurrently: PLATFORM(CAR.loc.final), EYETRACK(CAR) Example

37. Step 7: Instantiate the Template PARKING-VEHICLE Parameters: CAR, HOUSE, CAT Restrictions: CAR is a vehicle Preconditions: topic(CAR) or (ident(CAR) and position(CAR)) (ident(CAT) and position(CAT)) (ident(HOUSE) and position(HOUSE)) Effects: position(CAR), topic(CAR), express (park-23 agt:CAR loc:HOUSE,CAT ) Example Gaze Right Left Eyes follow right hand. Path of car, stop at Loc#2. To Loc#2

38. Step 8: Begin Planning Process PARKING-VEHICLE Parameters: CAR, HOUSE, CAT Restrictions: CAR is a vehicle Preconditions: topic(CAR) or (ident(CAR) and position(CAR)) (ident(CAT) and position(CAT)) (ident(HOUSE) and position(HOUSE)) Effects: position(CAR), topic(CAR), express (park-23 agt:CAR loc:HOUSE,CAT ) Example Gaze Right Left Eyes follow right hand. Path of car, stop at Loc#2. To Loc#2

39. Other Templates in the Database We’ve seen these: –PARKING-VEHICLE –PLATFORM –EYEGAZE There’s also these: –LOCATE-STATIONARY-ANIMAL –LOCATE-BULKY-OBJECT –MAKE-NOUN-SIGN Example

40. Step 9: Planning Continues… PARKING-VEHICLE Parameters: CAR, HOUSE, CAT Restrictions: CAR is a vehicle Preconditions:topic(CAR) or (ident(CAR) and position(CAR)) (ident(CAT) and position(CAT)) (ident(HOUSE) and position(HOUSE)) Effects: position(CAR), topic(CAR), express (park-23 agt:CAR loc:HOUSE,CAT ) Example Gaze Right Left Eyes follow right hand. Path of car, stop at Loc#2. To Loc#2 LOCATE-STATIONARY-ANIMAL Parameters: CAT Restrictions: CAT is an animal Preconditions:topic(CAT) Effects: topic(CAT), position(CAT), ident(CAT) Gaze Right Left Eyes at Cat Location. Move to Cat Location.

41. Step 9: Planning Continues… PARKING- VEHICLE MAKE- NOUN: “CAR” LOCATE- STATNRY- ANIMAL MAKE- NOUN: “CAT” LOCATE- BULKY- OBJECT MAKE- NOUN: “HOUSE” position(CAT) position(HOUSE) topic(CAR) identify(CAR) topic(CAT) identify(CAT) topic(HOUSE) identify(HOUSE) EYEGAZE PLATFORM (concurrently) Example

42. Gaze Right Left at Loc#1 at Loc#3 follow car Step 10: Build Phonological Spec PLATFORM EYEGAZE at viewer HOUSE at viewer CAT at viewer CAR MAKE- NOUN: “CAR” MAKE- NOUN: “CAT” MAKE- NOUN: “HOUSE” LOCATE- STATNRY- ANIMAL LOCATE- BULKY- OBJECT PARKING- VEHICLE Example

43. Wrap-Up and Discussion

44. Wrap-Up This is the first MT approach proposed for producing ASL Classifier Predicates. Currently in early implementation phase. Generation models for ASL CPs –discourse (topicalized/identified/positioned) –semantics (invisible ghosts) –syntax (planning operators) –phonology (simultaneous articulators)

45. Discussion ASL as an MT research vehicle –Need for a spatial representation to translate some English-to-ASL sentence pairs. –Virtual reality: intermediate MT representation. –A translation pathway tailored to a specific phenomenon as part of a multi-path system. –Symmetry in use of planning in the analysis and generation sides of the MT architecture.

46. Questions?