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Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daum III University of Maryland, College Park University of

  1. Feuding Families and Former Friends: 
 Unsupervised Learning for Dynamic Fictional Relationships Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daumé III University of Maryland, College Park University of Colorado, Boulder 1

  2. How can we describe a fictional relationship between two characters? 2

  3. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? 3

  4. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan) 4

  5. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan) 5

  6. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan) Frodo and Sam ( Lord of the Rings ) 6

  7. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan ) Frodo and Sam ( Lord of the Rings ) 7

  8. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan ) Frodo and Sam ( Lord of the Rings ) Winston and Julia ( 1984 ) 8

  9. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan ) Frodo and Sam ( Lord of the Rings ) Winston and Julia ( 1984 ) ??? 9

  10. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan ) Frodo and Sam ( Lord of the Rings ) Winston and Julia ( 1984 ) ??? Harry Potter and Sirius ( Prisoner of Azkaban ) 10

  11. How can we describe a fictional relationship between two characters? • isn’t this easy? we can assign it a single label (or relationship descriptor ) from a predetermined set Friend or foe? Peter Pan and Captain Hook ( Peter Pan ) Frodo and Sam ( Lord of the Rings ) Winston and Julia ( 1984 ) ??? Harry Potter and Sirius ( Prisoner of Azkaban ) ??? 11

  12. How can we describe a fictional relationship between two characters? • what if we treat relationships as sequences (or trajectories ) of descriptors? (Chaturvedi et al., 2016) Tom Sawyer and Becky Thatcher: friends -> foes -> friends 12

  13. How can we describe a fictional relationship between two characters? • what if we treat relationships as sequences (or trajectories ) of descriptors? (Chaturvedi et al., 2016) Tom Sawyer and Becky Thatcher: friends -> foes -> friends • limited by fixed descriptor set • required expensive annotations • limited to plot summaries 13

  14. passage of time Arthur and Lucy ( Dracula)

  15. I love him more than ever. We are to be married on 28 September. passage of time Arthur and Lucy ( Dracula)

  16. joy I love him more than ever. We are to be married on 28 September. marriage love passage of time Arthur and Lucy ( Dracula)

  17. I feel so weak and worn out … looked quite grieved … I hadn't the spirit joy I love him more than ever. We are to be married on 28 September. marriage love passage of time Arthur and Lucy ( Dracula)

  18. I feel so weak and worn out … looked quite grieved … I hadn't the spirit joy I love him more sickness than ever. We are to be married on 28 September. sadness marriage love love passage of time Arthur and Lucy ( Dracula)

  19. I feel so weak and worn poor girl, there is out … looked quite grieved peace for her at … I hadn't the spirit last. It is the end! joy I love him more sickness than ever. We are to be married on 28 September. sadness marriage love love passage of time Arthur and Lucy ( Dracula)

  20. I feel so weak and worn poor girl, there is out … looked quite grieved peace for her at … I hadn't the spirit last. It is the end! joy I love him more death sickness than ever. We are to be married on 28 September. fantasy sadness sickness marriage sadness love love love passage of time Arthur and Lucy ( Dracula)

  21. I feel so weak and worn poor girl, there is out … looked quite grieved peace for her at … I hadn't the spirit last. It is the end! joy Arthur placed the stake over her I love him more death heart … he struck sickness than ever. We are with all his might. to be married on The Thing in the 28 September. fantasy coffin writhed … sadness sickness marriage sadness love love love passage of time Arthur and Lucy ( Dracula)

  22. I feel so weak and worn poor girl, there is out … looked quite grieved peace for her at … I hadn't the spirit last. It is the end! joy death Arthur placed the stake over her I love him more death heart … he struck sickness than ever. We are with all his might. fantasy to be married on The Thing in the 28 September. fantasy coffin writhed … sadness sickness marriage murder sadness love love love love passage of time Arthur and Lucy ( Dracula)

  23. Why is this a worthwhile problem? • “Distant reading” (Moretti, 2005) can help humanities scholars collect examples of specific relationship types “Do Jane Austen’s female and male protagonists have a pattern in their evolving relationship (e.g., mutual disdain followed by romantic love)?” (Butler, 1975; Stovel, 1987; Hinant, 2006) “Do certain authors or novels portray relationships of desire more than others?” (Polhemus, 1990) “Can we detect positive or negative subtext underlying meals between two characters?” (Foster, 2009; Cognard-Black et al., 2014) 15

  24. Outline • Dataset: character interactions • RMN: relationship modeling network • Experiments: coherent descriptors, interpretable trajectories • Analysis: RMN’s strengths and weaknesses 16

  25. A Dataset of Character Interactions • For each pair of characters in a particular book, we extract all spans of text that contain mentions to both characters 17

  26. A Dataset of Character Interactions • For each pair of characters in a particular book, we extract all spans of text that contain mentions to both characters “If anyone was ever minding his business, it was I," Ignatius breathed. "Please. We must stop. I think I'm going to have a hemorrhage.” t=0 "Okay." Mrs. Reilly looked at her son's reddening face and realized that he would very happily collapse at her feet just to prove his point.” 17

  27. A Dataset of Character Interactions • For each pair of characters in a particular book, we extract all spans of text that contain mentions to both characters “If anyone was ever minding his business, it was I," Ignatius breathed. "Please. We must stop. I think I'm going to have a hemorrhage.” t=0 "Okay." Mrs. Reilly looked at her son's reddening face and realized that he would very happily collapse at her feet just to prove his point.” “ Ignatius belched the gas of a dozen brownies trapped by his valve. "Grant me a little peace.…” t=1 "You know I appreciate you, babe," Mrs. Reilly sniffed. "Come on and gimme a little goodbye kiss like a good boy.” 17

  28. A Dataset of Character Interactions • For each pair of characters in a particular book, we extract all spans of text that contain mentions to both characters “If anyone was ever minding his business, it was I," Ignatius breathed. "Please. We must stop. I think I'm going to have a hemorrhage.” t=0 "Okay." Mrs. Reilly looked at her son's reddening face and realized that he would very happily collapse at her feet just to prove his point.” “ Ignatius belched the gas of a dozen brownies trapped by his valve. "Grant me a little peace.…” t=1 "You know I appreciate you, babe," Mrs. Reilly sniffed. "Come on and gimme a little goodbye kiss like a good boy.” Mrs. Reilly looked at her son slyly and asked, " Ignatius , you sure you not a communiss?" "Oh, my God!" Ignatius bellowed. "Every t=2 day I am subjected to a McCarthyite witchhunt in this crumbling building. No!" 17

  29. A Dataset of Character Interactions • 1,383 novels from Project Gutenberg and other Internet sources • Genres represented include romance, mystery, and fantasy • Preprocessed with David Bamman’s BookNLP pipeline • Each span is a 200-token window centered around a character mention • 20,013 unique character pairs and 380,408 spans 18

  30. Relationship Modeling Network (RMN) • recurrent autoencoder with dictionary learning 19

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