2. Using Word-level Features to Better Predict Student Emotions during Spoken Tutoring Dialogues Mihai Rotaru Diane J. Litman Graduate Research Competition

3. 2 Breaking down the (long) title using WORD-LEVEL FEATURES STUDENT EMOTIONS SPOKEN TUTORING DIALOGUES TO PREDICT BETTER

4. 3 Tutoring dialogues Education Classroom setting Human (one-on-one) tutoring Computer tutoring (ITS – Intelligent Tutoring Systems) Addressing the learning gap between human and computer tutoring Improve the language understanding module of ITS Incorporate affective reasoning Adding human-provided emotional scaffolding to a reading tutor increases student persistence (Aist et al., 2002)

5. Human-Computer Excerpt Tutor26: Let me ask my question in a more general way. What happens to the velocity of a body when there is no force acting on it? Student27:dammit (ASR: it is) Tutor28 :Could you please repeat that? Student29 :same (ASR: i same) Tutor30 :Right. And what happens to the HORIZONTAL velocity of a body when there is no HORIZONTAL force acting on it? Student31:zero (ASR: the zero) Tutor32 :Well... OK, let's consider how forces in the vertical direction affect the motion of a body in a horizontal direction in a different scenario Student33:oh gravity you already said this (ASR: beats gravity you're exert less) Tutor34:Fine. Are there any other forces acting on the apple as it falls? Student35:no why are you doing this again (ASR: no y and to it yes) Tutor36:Correct. The only force acting on the apple as it falls to the earth is the earth's gravitational force. In which direction does gravitational force act? Student37:downward you computer (ASR: downward you computer)

6. 5 Affective reasoning Prerequisites Dialogue-based ITS : Why2 Interaction via speech : ITSPOKE (Intelligent Tutoring SPOKEn dialogue system) Affective reasoning Detect student emotions Handle student emotions

7. 6 Outline using WORD-LEVEL FEATURES STUDENT EMOTIONS SPOKEN TUTORING DIALOGUES TO PREDICT BETTER

8. 7 Student emotions Emotion annotation 3 Main emotion classes Negative - e.g. uncertain, bored, irritated, confused, sad Positive - e.g. confident, enthusiastic Neutral - no strong expression of negative or positive emotion; Predict EnE (Emotional, Non-Emotional) negatives and positives are conflated as Emotional neutrals are Non-Emotional useful for triggering system adaptation (HH corpus analysis) Corpora Human-Human (453 student turns from 10 dialogues) Human-Computer (333 student turns from 15 dialogues)

9. 8 Tutor: Uh let us talk of one car first. Student: ok. (EMOTION = NEUTRAL; Non-Emotional) Tutor: If there is a car, what is it that exerts force on the car such that it accelerates forward? Student: The engine. (EMOTION = POSITIVE; Emotional) Tutor: Uh well engine is part of the car, so how can it exert force on itself? Student: um… (EMOTION = NEGATIVE; Emotional) Annotation example

10. 9 Outline using WORD-LEVEL FEATURES STUDENT EMOTIONS SPOKEN TUTORING DIALOGUES TO PREDICT BETTER

11. 10 Predicting student emotion Lexical (word choice) Prosodic Dialogue context Others Tutor: Uh let us talk of one car first. Student: ok. Tutor: If there is a car, what is it that exerts force on the car such that it accelerates forward? Student: The engine. Tutor: Uh well engine is part of the car, so how can it exert force on itself? Student: um… 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 Features Previous work Mostly turn-level Very few word-level No comparison between turn-level and word-level

12. 11 Why word-level features? Emotion might not be expressed over the entire turn “This is great” Non-emotional words might wash out the effect of emotional words DispiritedHappy

13. 12 Why word-level features? (2) Can approximate pitch contour better at sub-turn levels. Especially for longer turns This is great

14. 13 Extended pitch features set Previous work Min, Max Avg, Stdev Extend with Start, End Regression coefficient and regression error Quadratic regression coefficient from Batliner et al. 2003

15. 14 But wait… Student turn 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 Features Turn emotional class Machine learning Word 1 … Word n 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 … ? Turn-level Word-level 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 Turn emotional class Machine learning Sönmez et al., 1998

16. 15 Word-level emotion model Student turn 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 Features Machine learning Word 1 … Word n 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 321654615, asdakd, 342.234234 Asdhkas, a34334, 324,7657755 … Word-level emotion … Turn-level Word-level Turn emotional class

17. 16 Word-level emotion model Training phase Each word labeled with turn class Extra features to identify the position of the word in the turn (distance in words from the beginning and end of the turn) Learn emotion model at the word level Test phase Predict each word class based on the learned model Use majority/weighted voting to label the turn based on its word classes Ties are broken randomly

18. 17 Outline using WORD-LEVEL FEATURES STUDENT EMOTIONS SPOKEN TUTORING DIALOGUES TO PREDICT BETTER

19. 18 Questions to answer Will word level feature work better than turn level features for emotion prediction? Yes If yes, where does the advantage comes from? Better prediction of longer turns Is there a feature set that offers robust performance? Yes. Combination of pitch and lexical features at word level.

20. 19 Experiment setup Two contrasting corpora Two contrasting learners (WEKA) IB1 – nearest neighbor classifier ADA – boosted decision trees

21. 20 Feature sets Only pitch and lexical features 6 sets of features Turn level: Lex-Turn – only lexical Pitch-Turn – only pitch PitchLex-Turn – lexical and prosodic Word level: Lex-Word – only lexical + positional Pitch-Word – only pitch + positional PitchLex-Word – lexical and prosodic + positional Baseline: majority class 10 x 10 cross validation

22. 21 Results – IB1 on HH Word-level features significantly outperform turn-level features Word-level better than turn-level on longer turns Best performers: Lex-Word, PitchLex-Word

23. 22 Results – ADA on HH Turn-level performance increases a lot Word-level significantly better than turn-level on features sets with pitch Word-level better than turn-level on longer turns but the difference is smaller Best performers: Lex-Turn, Lex-Word, PitchLex-Word

24. 23 Results – IB1 on HC Word-level features significantly outperform turn-level features Lexical information less helpful than on HH corpus Word-level better than turn-level on longer turns Best performers: Pitch-Word, PitchLex-Word

25. 24 Results – ADA on HC Difference not significant anymore IB1 better than ADA on word-level features ADA has bigger variance on this corpus Word-level better than turn-level on longer turns but the difference is smaller Best performers: Pitch-Turn, Pitch-Word, PitchLex-Turn, PitchLex-Word

26. 25 Discussion Lexical features at turn and word-level are similar Performance dependent on corpus and learner Pitch features differ significantly Word-level better than turn-level (4/6) PitchLex-Word a consistent best performer Our best accuracies comparable with previous work

27. 26 Conclusions & Future work Word-level better than turn-level for emotion prediction Even under a very simple word-level emotion model Word-level better at predicting longer turns PitchLex-Word a consistent best performer Future work: More refined word-level emotion models HMMs Co-training Filter irrelevant words Use the prosodic information left out See if our conclusions generalize on detecting student uncertainty Experiment with other sub-turn units (breath groups)

28. 27 Acknowledgements ITSPOKE group Diane Litman, Hua AI, Kate Forbes-Riley, Beatriz Maeireizo- Tokeshi, Amruta Purandare, Scott Silliman, Joel Tetreault, Art Ward NLP group People who heard my presentation so many times

29. 28 Back-end is Why2-Atlas system (VanLehn et al., 2002) Sphinx2 speech recognition and Cepstral text-to-speech

30. 29 Back-end is Why2-Atlas system (VanLehn et al., 2002) Sphinx2 speech recognition and Cepstral text-to-speech

31. 30 Back-end is Why2-Atlas system (VanLehn et al., 2002) Sphinx2 speech recognition and Cepstral text-to-speech

32. 31 Feature Extraction per Student Turn Five feature types acoustic-prosodic (1) non acoustic-prosodic lexical (2) other automatic (3) manual (4) identifiers (5) Research questions utility of different features speaker and task dependence

33. 32 Feature Types (1) Acoustic-Prosodic Features (normalized)  4 pitch (f0) : max, min, mean, standard dev.  4 energy (RMS) : max, min, mean, standard dev.  4 temporal: turn duration (seconds) pause length preceding turn (seconds) tempo (syllables/second) internal silence in turn (zero f0 frames)  available to ITSPOKE in real time

34. 33 Feature Types (2) Lexical Items  word occurrence vector

35. 34 Feature Types (3) Other Automatic Features: available from ITSPOKE logs  Turn Begin Time (seconds from dialog start)  Turn End Time (seconds from dialog start)  Is Temporal Barge-in (student turn begins before tutor turn ends)  Is Temporal Overlap (student turn begins and ends in tutor turn)  Number of Words in Turn  Number of Syllables in Turn

36. 35 Feature Types (4 ) Manual Features: (currently) available only from human transcription  Is Prior Tutor Question (tutor turn contains “?”)  Is Student Question (student turn contains “?”)  Is Semantic Barge-in (student turn begins at tutor word/pause boundary)  Number of Hedging/Grounding Phrases (e.g. “mm- hm”, “um”)  Is Grounding (canonical phrase turns not preceded by a tutor question)  Number of False Starts in Turn (e.g. acc-acceleration)