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Generation Models for American Sign Language Classifier Predicates Matt Huenerfauth Penn Computational Linguistics Lunch November 1, 2004 Research Advisors:

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Presentation on theme: "Generation Models for American Sign Language Classifier Predicates Matt Huenerfauth Penn Computational Linguistics Lunch November 1, 2004 Research Advisors:"— Presentation transcript:

1 Generation Models for American Sign Language Classifier Predicates Matt Huenerfauth Penn Computational Linguistics Lunch November 1, 2004 Research Advisors: Mitch Marcus & Martha Palmer Computer and Information Science University of Pennsylvania Adapted from presentations given at: The 6th International ACM SIGACCESS Conference on Computers and Accessibility, October 20, 2004, Atlanta, GA The 10th International Conference on Theoretical and Methodological Issues in Machine Translation, October 4, 2004, Baltimore, MD

2 English-to-ASL MT Development of English-to-ASL machine translation (MT) software for accessibility applications has been slow… –Misconceptions: the deaf experience, ASL linguistics, and ASL’s relationship to English. –Challenges: some ASL phenomena are very difficult (but important) to translate. We’ve had to develop some new models for ASL generation.

3 Misconceptions about Deaf Literacy and ASL MT How have they affected research?

4 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.

5 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…

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

7 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.”

8 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)

9 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.

10 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 “Sign Frequency Characteristics of ASL.” Sign Language Studies. 3:2. Misconception: OK to ignore visual/spatial ASL phenomena.

11 ASL MT Challenges: Producing Classifier Predicates A new set of generation models…

12 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 “A Multi-Path Architecture for English-to-ASL MT.” HLT-NAACL Student Workshop.

13 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 “A Multi-Path Architecture for English-to-ASL MT.” HLT-NAACL Student Workshop.

14 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

15 Design of the CPML Pathway English Sentence Pred-Arg Structure 3D Animation Planning Operator 3D Animation of the Event CP Semantics CP Syntax CP Phonology CP Discourse

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

17 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

18 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.

19 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

20 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?

21 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.

22 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

23 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 "Dynamically Altering Agent Behaviors Using Nat. Lang. Instructions." Int'l Conf. on Autonomous Agents. Related Work: Coyne and Sproat “WordsEye: An Automatic Text-to-Scene Conversion System.” SIGGRAPH Los Angeles, CA.

24 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.)

25 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

26 Step 2: AnimNL builds 3D scene Example

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

28 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

29 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

30 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.

31 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

32 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

33 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

34 Step 4: Producing Ghost Scene Example HOUSE CAR CAT

35 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

36 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 “Spatial Representation of Classifier Predicates for MT into ASL.” Workshop on Representation and Processing of Signed Languages, LREC Liddel, S Grammar, Gesture, and Meaning in ASL. Cambridge University Press.

37 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

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. Generation models for ASL CPs –discourse (topicalized/identified/positioned) –semantics (invisible ghosts) –syntax (planning operators) –phonology (simultaneous articulators)

50 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.

51 Positive Implications of this Design ASL animation for 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)

52 Questions?

53 Extra Slides

54 CPs are important for ASL on user-interface –Can’t “write” ASL on buttons and menus. –Character must refer to parts of screen. –ASL uses CPs to do this. “Grab” GUI screen coordinates to lay out our placeholders (the little red dots). –Don’t need AnimNL software. –Can update placeholders dynamically. Advantage: Producing ASL animation for user-interfaces.

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56 Original Image: Simon the Signer (Bangham et al )

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61 Other national sign languages have their own signs and structure distinct from ASL. However, nearly all have a system of classifier predicates that is similar to ASL. –The specific handshapes used might differ, as well as some other motion details. –Future Potential: But this 3D approach to CP production should be easy to adapt to these other sign languages. Advantage: Create CPs for other national sign languages.

62 There are other linguistic phenomena in ASL (aside from classifier predicates) that could benefit from the way this system keeps track of the space around the signer. –Pronouns –Verb Agreement –Narrative Role-Shifting –Contrastive Discourse Structure Advantage: Producing non-CP ASL linguistic phenomena.

63 Tactile Sign Language –Feel the signer’s hands move through 3D space. This system: unique in use of virtual reality. –Graphics software arranges the 3D objects. –Signing character is also a detailed 3D object. Future Potential: –ASL  Tactile Sign Language –A deaf-blind user could experience the motion of signer’s hands using tactile feedback glove. Potential Future Extension: Sign language virtual reality for the deaf-blind

64 Several approaches to generating the 3D motion path of the hands were examined. * –For linguistic and engineering reasons, several simplistic approaches were discounted: Pre-storing all possible motion paths. Rule-based approach to construct motion paths. –To produce CPs, the system needs to model the 3D layout of the objects under discussion. Challenge: Calculating 3D motion paths is difficult. * Huenerfauth, M “Spatial Representation of Classifier Predicates for MT into ASL.” Workshop on Representation and Processing of Signed Languages, LREC-2004.

65 Some CP motion paths are linguistically, not visually determined. –E.g. Leisurely walking upright figure. –Motion of 3D character ≠ Motion of hand. Solution: Store CPs as a set of templates. –Template represents prototypical form of a CP. –Fill in 3D coordinate details at run-time. –Some 3D paths taken from the virtual reality. –Some 3D info hard-coded inside the template. Challenge: Some motion paths linguistically determined.

66 Sometimes no one-to-one mapping from English sentences to classifier predicates. Solution: Use same formalism to represent the structure inside and in-between CPs. –Makes it easy to store one-to-one, many-to-one, one-to-many, and many-to-many mappings. –Allows one CP to affect production of another. –Allows CPs to work together to convey info. Challenge: English-to-ASL not always 1-to-1 mapping.


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