Detecting Action Items in Multi-Party Meetings: Annotation and Initial Experiments

1. Detecting Action Items in Multi-Party Meetings: Annotation and Initial Experiments Matthew Purver, Patrick Ehlen, John Niekrasz Computational Semantics Laboratory Center for the Study of Language and Information Stanford University

2. The CALO Project • Multi-institution, multi-disciplinary project • Working towards an intelligent personal assistant that learns • Three major areas • managing personal data • clustering email, documents, managing contacts • assisting with task execution • learning to carry out computer-based tasks • observing interaction in meetings

3. The CALO Meeting Assistant • Observe human-human meetings • Audio recording & speech recognition (ICSI/CMU) • Video recording & processing (MIT/CMU) • Written notes, via digital ink (NIS) or typed (CMU) • Whiteboard sketch recognition (NIS) • Produce a useful record of the interaction • answer questions about what happened • can be used by attendees or non-attendees • Learn to do this better over time (LITW)

4. The CALO Meeting Assistant • Primary focus on end-user • Develop something that can really help people when it comes to dealing with all of the meetings we have to deal with

5. What do people want to know from meetings?

6. What do people want to know from meetings? • Banerjee et al. (2005) survey of 12 academics: • Missed meeting - what do you want to know? • Topics: which were discussed, what was said? • Decisions: what decisions were made? • Action items/tasks: was I assigned something?

7. What do people want to know from meetings? • Banerjee et al. (2005) survey of 12 academics: • Missed meeting - what do you want to know? • Topics: which were discussed, what was said? • Decisions: what decisions were made? • Action items/tasks: was I assigned something? • Lisowska et al. (2004) survey of 28 people: • What would you ask a meeting reporter system? • Similar responses about topics, decisions • who attended, who asked/decided what? • Did they talk about me?

8. Purpose • Helpful system: not only records and transcribes a meeting, but extracts (from streams of potentially messy human-human speech): • topics discussed • decisions made • tasks assigned (“action items”) • The system should highlight this information over meeting “noise”

9. Example • Impromptu meeting you might have after your team has boarded a rebel spacecraft in search of stolen plans, and you’re trying to figure out what to do next

10. Commander, tear this ship apart until you’ve found those plans!

11. Commander, tear this ship apart until you’ve found those plans!

12. Commander, tear this ship apart until you’ve found those plans! A section of discourse in a meeting where someone is made responsible to take care of something

14. Action Items • Concrete decisions; public commitments to be responsible for a particular task • Want to know: • Can we find them? • Can we produce useful descriptions of them? • Not aware of previous discourse-based work

15. Action Item Detection in Email • Corston-Oliver et al., 2004 • Marked a corpus of email with “dialogue acts” • Task act: • “items appropriate to add to an ongoing to-do list” • Good inter-annotator agreement (kappa > 0.8) • Per-sentence classification using SVMs • lexical features e.g. n-grams; punctuation; message features • f-scores around 0.6

16. A First Try: Flat Annotation • Gruenstein et al (2005) analyzed 65 meetings annotated from: • ICSI Meeting Corpus (Janin et al., 2003) • ISL Meeting Corpus (Burger et al., 2002) • Two human annotators • “Mark utterances relating to action items” • create groups of utterances for each AI • made no distinction between utterance type/role

17. A First Try: Flat Annotation (cont’d) • Annotators identified 921 / 1267 (respectively) action item-related utterances • Human agreement poor ( < 0.4) • Tried binary classification using SVMs (like Corston-Oliver) • Precision, recall, f-score: all below .25

18. Try a more restricted dataset? • Sequence of 5 (related) CALO meetings • similar amount of ICSI/ISL data for training • Same annotation schema • SVMs with words & n-grams as features • Also tried other discriminative classifiers, and 2- & 3- grams, w/ no improvements • Similar performance • Improved f-scores (0.30 - 0.38), but still poor • Recall up to 0.67, precision still low (< 0.36)

19. Should we be surprised? • Our human annotator agreement poor • DAMSL schema has dialogue acts Commit, Action-directive • annotator agreement poor ( ~ 0.15) • (Core & Allen, 1997) • ICSI MRDA dialogue act commit • Most DA tagging work concentrates on 5 broad DA classes • Perhaps “action items” comprise a more heterogeneous set of utterances

20. Rethinking Action Item Acts • Maybe action items are not aptly described as singular “dialogue acts” • Rather: multiple people making multiple contributions of several types • Action item-related utterances represent a form of group action, or social action • That social action has several components, giving rise to a heterogeneous set of utterances • What are those components?

21. A person commits or is committed to “own” the action item Commander, tear this ship apart until you’ve found those plans!

22. A person commits or is committed to “own” the action item A description of the task itself is given Commander, tear this ship apart until you’ve found those plans!

23. A person commits or is committed to “own” the action item A description of the task itself is given A timeframe is specified Commander, tear this ship apart until you’ve found those plans!

24. A person commits or is committed to “own” the action item A description of the task itself is given A timeframe is specified Some form of agreement Yes, Lord Vader!

25. Exploiting discourse structure • Action items have distinctive properties • Task description, owner, timeframe, agreement • Action item utterances can simultaneously play different roles • assigning properties • agreeing/committing • These classes may be more homogeneous & distinct than looking for just “action item” utts. • Could improve classification performance

26. New annotation schema • Annotated and classified again using the new schema • Classify utterances by their role in the action item discourse • can play more than one role • Define action items by grouping subclass utterances together in an action-item discussion • a subclass can be missing

27. Action Item discourse: an example

28. New Experiment • Annotated same set of CALO/ICSI/ISL data using the new schema • Ran classifiers to train and identify utterances that contain each of the 4 subclasses

29. Encouraging signs • Between-class distinction (cosine distances) • Agreement vs. any other is good: 0.05 to 0.12 • Timeframe vs. description is OK: 0.25 • Owner/timeframe/description: 0.36 to 0.47 • Improved inter-annotator agreement? • Timeframe:  = 0.86 • Owner 0.77, agreement & description 0.73 • Warning: this is only on one meeting, although it’s the most difficult one we could find

30. Combined classification • Still don’t have enough data for proper combined classification • Recall 0.3 to 0.5, precision 0.1 to 0.5 • Agreement subclass is best, with f-score = 0.40 • Overall decision based on sub-classifier outputs • Ad-hoc heuristic: • prior context window of 5 utterances • agreement plus one other class

31. Questions we can ask • Does overall classification look useful? • Whole-AI-based f-score 0.40 to 1.0 (one meeting perfectly correlated with human annotation) • Does overall output improve sub-classifiers? • Agreement: f-score 0.40  0.43 • Timescale: f-score 0.26  0.07 • Owner: f-score 0.12  0.24 • Description: f-score 0.33  0.24

32. Example output • From a CALO meeting: • t = [the, start, of, week, three, just, to] • o = [reconfirm, everything, and, at, that, time, jack, i'd, like, you, to, come, back, to, me, with, the] • d = [the, details, on, the, printer, and, server] • a = [okay] • Another (less nice?) example: • o = [/h#/, so, jack, /uh/, for, i'd, like, you, to] • d = [have, one, more, meeting, on, /um/, /h#/, /uh/] • t = [in, in, a, couple, days, about, /uh/] • a = [/ls/, okay]

33. Where next for action items? • More data annotation • Using NOMOS, our annotation tool • Meeting browser to get user feedback • Improved individual classifiers • Improved combined classifier • maximum entropy model • not enough data yet • Moving from words to symbolic output • Gemini (Dowding et al., 1990) bottom-up parser

35. Questions we can ask • Does overall classification look useful? • Whole-AI-based f-score 0.40 to 1.0 (one meeting perfectly correlated with human annotation) • Does overall output improve sub-classifiers? • Agreement: f-score 0.40  0.43 • Timescale: f-score 0.26  0.07 • Owner: f-score 0.12  0.24 • Description: f-score 0.33  0.24