1. Computational Linguistic Models of American Sign Language Classifier Predicates Matt Huenerfauth Penn Working Group in Language Spring Symposium University of Pennsylvania, April 2, 2005 Research Advisors: Mitch Marcus & Martha Palmer Computer and Information Science University of Pennsylvania

2. 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 using ASL animation. –Access to information/media. Audiology Online Misconception: All deaf people are written-English literate.

3. 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 Building tools to address deaf literacy…

4. Photos: Seamless Solutions, Inc. Simon the Signer (Bangham et al. 2000.) Vcom3D Corporation We can use an off-the-shelf animated character. Building tools to address deaf literacy…

5. Misconception: ASL is just manually performed English. Signed English vs. American Sign Language. Some ASL sentences have a structure that is similar to written languages. Other sentences use space around signer to describe 3D layout of a real-world scene. –Hands indicate movement and location of entities in the scene (using special handshapes). –These are 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. Misconception: Traditional MT software is well-suited to ASL. Classifier predicates are hard to produce. –3D paths for the hands, layout of the scene. –Grammar rules & lexicons? Not enough. No written form of ASL. –Very little English-ASL parallel corpora. –Can’t use machine learning approaches. Previous systems are only partial solutions. –Some produce only Signed English, not ASL. –None can produce classifier predicates.

8. But classifier predicates are important! –CPs are needed to convey many concepts. –Signers use CPs frequently. * –English sentences that produce CPs are the ones that signers often have trouble reading. –CPs needed for some important applications User-interfaces with ASL animation Literacy educational software * Morford and McFarland. 2003. “Sign Frequency Characteristics of ASL.” Sign Language Studies. 3:2. Misconception: OK to ignore visual/spatial ASL phenomena.

9. Producing Classifier Predicates A new set of generation models…

10. Focus on Classifier Predicates Previous ASL MT systems have shown promise at handling non-spatial ASL phenomena using traditional MT technologies. This project will focus on producing the spatially complex elements of the language: Classifier Predicates of Movement and Location (CPMLs). Since some of these new MT methods for CPMLs are computationally expensive, we’ve proposed a multi-path * MT design. * Huenerfauth, M. 2004. “A Multi-Path Architecture for English-to-ASL MT.” HLT-NAACL Student Workshop.

11. English Input Sentences 3D Software Traditional MT Software Word-to-Sign Look-up Spatially descriptive English sentences… Most English sentences… Sentences that the MT software cannot successfully translate… ASL sentence containing a classifier predicate ASL sentence not containing a classifier predicate Signed English Sentence * Huenerfauth, M. 2004. “A Multi-Path Architecture for English-to-ASL MT.” HLT-NAACL Student Workshop.

12. CPML Generation Models What are the representations used in the English to CPML pathway? English Input Sentences 3D Software Spatially descriptive English sentences… ASL sentence containing a classifier predicate

13. Design of the CPML Pathway English Sentence Predicate-Argument Structure 3D Animation Planning Operator 3D Animation of the Event CP Semantics CP Syntax CP Phonology CP Discourse

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

15. 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

16. 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.

17. 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

18. 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?

19. 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.

20. 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

21. 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.

22. 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.)

23. Step 1: Analyzing English Input The car parked between the cat and the house. Syntactic analysis (build a parse tree). 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

24. Step 2: AnimNL builds 3D scene Example

25. Step 2: AnimNL builds 3D scene Original Image: Simon the Signer (Bangham et al. 2000.)

26. 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

27. 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

28. 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.

29. 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

30. 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

31. 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

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

33. 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

34. 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.

35. 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 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

36. A little more about planning… Planning is a special form of search process in which you specify a goal to be achieved (in some logical language). You may begin the process in some initial state (also specified in this logical language). Then, you define a set of “planning operators” which represent actions that you can take. These have preconditions and effects (also specified in this logical language). –This makes it a little different than normal search.

37. A little more about planning… During planning, you begin with your goal, and you try different combinations of planning operators to satisfy the goal. These may have preconditions which in turn must be satisfied by other planning operators. The goal may be decomposed into sets of subgoals, and you can try satisfying each of these individually. An individual planning operator may also specify a complex set of sub-actions. In the end, you produce a schedule of actions that should be taken in order to achieve the goal.

38. 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)

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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.

46. 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

47. 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

48. Wrap-Up and Discussion

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

50. Positive Implications of this Design ASL animation for computer user-interfaces. –CPMLs are important to generate. –Use GUI coordinates to arrange ghosts. Non-CPML ASL linguistic phenomena that make use of space. –The model of space helps us generate them. CPMLs in other national sign languages. –Some differences, but many similarities to ASL. (extra slides)

51. Questions?