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Learning to Sportscast: A Test of Grounded Language Acquisition

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Presentation on theme: "Learning to Sportscast: A Test of Grounded Language Acquisition"— Presentation transcript:

1 Learning to Sportscast: A Test of Grounded Language Acquisition
David Chen & Raymond Mooney Department of Computer Sciences University of Texas at Austin

2 Motivation Constructing annotated corpora for language learning is difficult Children acquire language through exposure to linguistic input in the context of a rich, relevant, perceptual environment

3 Goals Learn to ground the semantics of language Block
Learn language through correlated linguistic and visual inputs

4 Challenge

5 Challenge

6 Challenge A linguistic input may correspond to many possible events
? Block ? ?

7 Overview Sportscasting task Tactical generation Strategic generation
Human evaluation

8 Learning to Sportscast
Robocup Simulation League games No speech recognition Record commentaries in text form No computer vision Ruled-based system to automatically extract game events in symbolic form Concentrate on linguistic issues

9 Robocup Simulation League

10 Robocup Simulation League
Pink4’s pass was intercepted by Purple6

11 Learning to Sportscast
Learn to sportscast by observing sample human sportscasts Build a function that maps between natural language (NL) and meaning representation (MR) NL: Textual commentaries about the game MR: Predicate logic formulas that represent events in the game

12 Mapping between NL/MR NL: “Purple3 passes the ball to Purple5”
Tactical Generation (MR  NL) Semantic Parsing (NL  MR) MR: Pass ( Purple3, Purple5 )

13 Robocup Sportscaster Trace
Natural Language Commentary Meaning Representation badPass ( Purple1, Pink8 ) turnover ( Purple1, Pink8 ) Purple goalie turns the ball over to Pink8 kick ( Pink8) pass ( Pink8, Pink11 ) Purple team is very sloppy today kick ( Pink11 ) Pink8 passes the ball to Pink11 Pink11 looks around for a teammate kick ( Pink11 ) ballstopped kick ( Pink11 ) Pink11 makes a long pass to Pink8 pass ( Pink11, Pink8 ) kick ( Pink8 ) pass ( Pink8, Pink11 ) Pink8 passes back to Pink11 13

14 Robocup Sportscaster Trace
Natural Language Commentary Meaning Representation badPass ( Purple1, Pink8 ) turnover ( Purple1, Pink8 ) Purple goalie turns the ball over to Pink8 kick ( Pink8) pass ( Pink8, Pink11 ) Purple team is very sloppy today kick ( Pink11 ) Pink8 passes the ball to Pink11 Pink11 looks around for a teammate kick ( Pink11 ) ballstopped kick ( Pink11 ) Pink11 makes a long pass to Pink8 pass ( Pink11, Pink8 ) kick ( Pink8 ) pass ( Pink8, Pink11 ) Pink8 passes back to Pink11 14

15 Robocup Sportscaster Trace
Natural Language Commentary Meaning Representation badPass ( Purple1, Pink8 ) turnover ( Purple1, Pink8 ) Purple goalie turns the ball over to Pink8 kick ( Pink8) pass ( Pink8, Pink11 ) Purple team is very sloppy today kick ( Pink11 ) Pink8 passes the ball to Pink11 Pink11 looks around for a teammate kick ( Pink11 ) ballstopped kick ( Pink11 ) Pink11 makes a long pass to Pink8 pass ( Pink11, Pink8 ) kick ( Pink8 ) pass ( Pink8, Pink11 ) Pink8 passes back to Pink11 15

16 Robocup Sportscaster Trace
Natural Language Commentary Meaning Representation P6 ( C1, C19 ) P5 ( C1, C19 ) Purple goalie turns the ball over to Pink8 P1( C19 ) P2 ( C19, C22 ) Purple team is very sloppy today P1 ( C22 ) Pink8 passes the ball to Pink11 Pink11 looks around for a teammate P1 ( C22 ) P0 P1 ( C22 ) Pink11 makes a long pass to Pink8 P2 ( C22, C19 ) P1 ( C19 ) P2 ( C19, C22 ) Pink8 passes back to Pink11 16

17 Robocup Data Collected human textual commentary for the 4 Robocup championship games from Avg # events/game = 2,613 Avg # sentences/game = 509 Each sentence matched to all events within previous 5 seconds. Avg # MRs/sentence = 2.5 (min 1, max 12) Manually annotated with correct matchings of sentences to MRs (for evaluation purposes only).

18 Overview Sportscasting task Tactical generation Strategic generation
Human evaluation

19 Tactical Generation Learn how to generate NL from MR Example:
Two steps Disambiguate the training data Learn a language generator Pass(Pink2, Pink3)  “Pink2 kicks the ball to Pink3”

20 System Overview Ambiguous Training Data Sportscaster Robocup Simulator
Pass ( Purple5, Purple7 ) Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Ambiguous Training Data

21 Semantic Parser Learner
System Overview Sportscaster Robocup Simulator Pass ( Purple5, Purple7 ) Initial Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Semantic Parser Learner Ambiguous Training Data

22 System Overview Unambiguous Training Data Initial Semantic Parser
Purple7 loses the ball to Pink2 Kick ( pink2 ) Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Kick ( pink5 ) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Initial Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Ambiguous Training Data

23 Semantic Parser Learner
System Overview Purple7 loses the ball to Pink2 Kick ( pink2 ) Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Kick ( pink5 ) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Semantic Parser Learner Ambiguous Training Data

24 Semantic Parser Learner
System Overview Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Kick ( pink5 ) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Semantic Parser Learner Ambiguous Training Data

25 Semantic Parser Learner
System Overview Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Kick ( pink5 ) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Semantic Parser Learner Ambiguous Training Data

26 Semantic Parser Learner
System Overview Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Pass ( pink5 , pink8) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Semantic Parser Learner Ambiguous Training Data

27 Semantic Parser Learners
Learn a function from NL to MR NL: “Purple3 passes the ball to Purple5” Semantic Parsing (NL  MR) Tactical Generation (MR  NL) MR: Pass ( Purple3, Purple5 ) We experiment with two semantic parser learners WASP (Wong & Mooney, 2006; 2007) KRISP (Kate & Mooney, 2006)

28 WASP: Word Alignment-based Semantic Parsing
Uses statistical machine translation techniques Synchronous context-free grammars (SCFG) (Wu, 1997; Melamed, 2004; Chiang, 2005) Word alignments (Brown et al., 1993; Och & Ney, 2003) Capable of both semantic parsing and tactical generation

29 KRISP: Kernel-based Robust Interpretation by Semantic Parsing
Productions of MR language are treated like semantic concepts SVM classifier is trained for each production with string subsequence kernel These classifiers are used to compositionally build MRs of the sentences More resistant to noisy supervision but incapable of tactical generation

30 Matching Ability to find correct NL/MR pair
4 Robocup championship games from Avg # events/game = 2,613 Avg # sentences/game = 509 Leave-one-game-out cross-validation Metric: Precision: % of system’s annotations that are correct Recall: % of gold-standard annotations correctly produced F-measure: Harmonic mean of precision and recall

31 WASP’s language generator
Systems Learner KRISPER (Kate & Mooney, 2007) KRISP WASPER WASP WASPER-GEN WASP’s language generator

32 Semantic Parser Learner
KRISPER and WASPER Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Kick ( pink5 ) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Semantic Parser Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Semantic Parser Learner (KRISP/WASP) Ambiguous Training Data

33 WASP’s language generator
Systems Learner KRISPER (Kate & Mooney, 2007) KRISP WASPER WASP WASPER-GEN WASP’s language generator

34 Tactical Generator Learner
WASPER-GEN Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Sportscaster Robocup Simulator Pink2 kicks the ball to Pink5 Pass ( pink2 , pink5 ) Pink5 makes a long pass to Pink8 Kick ( pink5 ) Pink8 shoots the ball Kick ( pink8 ) Unambiguous Training Data Pass ( purple5, purple7 ) Tactical Generator Turnover ( purple7 , pink2 ) Purple7 loses the ball to Pink2 Kick ( pink2 ) Pass ( pink2 , pink5 ) Pink2 kicks the ball to Pink5 Kick ( pink5 ) Pass ( pink5 , pink8) Pink5 makes a long pass to Pink8 Ballstopped Kick ( pink8 ) Pink8 shoots the ball Tactical Generator Learner (WASP) Ambiguous Training Data

35 Matching Results

36 Overview Sportscasting task Tactical generation Strategic generation
Human evaluation

37 Strategic Generation Generation requires not only knowing how to say something (tactical generation) but also what to say (strategic generation). For automated sportscasting, one must be able to effectively choose which events to describe.

38 Example of Strategic Generation
pass ( purple7 , purple6 ) ballstopped kick ( purple6 ) pass ( purple6 , purple2 ) kick ( purple2 ) pass ( purple2 , purple3 ) kick ( purple3 ) badPass ( purple3 , pink9 ) turnover ( purple3 , pink9 )

39 Example of Strategic Generation
pass ( purple7 , purple6 ) ballstopped kick ( purple6 ) pass ( purple6 , purple2 ) kick ( purple2 ) pass ( purple2 , purple3 ) kick ( purple3 ) badPass ( purple3 , pink9 ) turnover ( purple3 , pink9 )

40 Strategic Generation For each event type (e.g. pass, kick) estimate the probability that it is described by the sportscaster. Requires correct NL/MR matching Use estimated matching from tactical generation Iterative Generation Strategy Learning

41 Iterative Generation Strategy Learning (IGSL)
Directly estimates the likelihood of an event being commented on Self-training iterations to improve estimates Uses events not associated with any NL as negative evidence

42 Strategic Generation Performance
Evaluate how well the system can predict which events a human comments on Metric: Precision: % of system’s annotations that are correct Recall: % of gold-standard annotations correctly produced F-measure: Harmonic mean of precision and recall

43 Strategic Generation Results

44 Overview Sportscasting task Tactical generation Strategic generation
Human evaluation

45 Human Evaluation (Quasi Turing Test)
4 fluent English speakers as judges 8 commented game clips 2 minute clips randomly selected from each of the 4 games Each clip commented once by a human, and once by the machine Presented in random counter-balanced order Judges were not told which ones were human or machine generated

46 Demo Clip Game clip commentated using WASPER-GEN with IGSL, since this gave the best results for generation. FreeTTS was used to synthesize speech from textual output.

47 Human Evaluation Score English Fluency Semantic Correctness
Sportscasting Ability 5 Flawless Always Excellent 4 Good Usually 3 Non-native Sometimes Average 2 Disfluent Rarely Bad 1 Gibberish Never Terrible 47

48 Human Evaluation Commentator English Fluency Semantic Correctness
Score English Fluency Semantic Correctness Sportscasting Ability 5 Flawless Always Excellent 4 Good Usually 3 Non-native Sometimes Average 2 Disfluent Rarely Bad 1 Gibberish Never Terrible Commentator English Fluency Semantic Correctness Sportscasting Ability Human 3.94 4.25 3.63 Machine 3.44 3.56 2.94 Difference 0.5 0.69 48

49 Future Work Expand MRs to beyond simple logic formulas
Apply approach to learning situated language in a computer video-game environment (Gorniak & Roy, 2005) Apply approach to captioned images or video using computer vision to extract objects, relations, and events from real perceptual data (Fleischman & Roy, 2007)

50 Conclusion Current language learning work uses expensive, unrealistic training data. We have developed a language learning system that can learn from language paired with an ambiguous perceptual environment. We have evaluated it on the task of learning to sportscast simulated Robocup games. The system learns to sportscast almost as well as humans.

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