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CS5545: Natural Language Generation
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  1. CS5545: Natural Language Generation Background Reading: Reiter and Dale, Building Natural Language Generation Systems, chaps 1, 2

  2. Words instead of Pictures • Natural Language Generation (NLG): Generate English sentences to communicate data, instead of visualisations (or tables) • A research focus of Aberdeen CS Dept

  3. Example : FoG • Produces textual weather reports in English and French • Input: • Graphical/numerical weather depiction • User: • Environment Canada (Canadian Weather Service)

  4. FoG: Input

  5. FoG: Output

  6. Why use words? • Many potential reasons • Media restrictions (eg, text messages) • Users not knowledgeable enough to interpret a graph correctly • Words also communicate background info, emphasis, interpretation, … • People (in some cases) make better decisions from words than graphs

  7. Too hard for 1/3 of patients

  8. Easier for many people? • I’m afraid to say that you have a 1 in 3 chance of dying from a heart attack before your 65th birthday if you carry on as you are. But if you stop smoking, take your medicine, and eat better, a fatal heart attack will be much less likely (only a 1 in 12 chance).

  9. Text vs Graph • Focus on key info (absolute risk, optimum risk) • Integrate with explanation (optimum risk means if you stop smoking, eat better, take medicine) • Add emphasis, perspective, “spin” (eg. “I’m afraid to say” indicates this is a serious problem)

  10. Experiment: Decision Making • Showed 40 medical professionals (from junior nurses to senior doctors) data from a baby in neonatal ICU • Text summary of graphical depiction • Asked to make a treatment decision • Better decision when shown text • But said they preferred the graphic

  11. Graphic Depiction

  12. Text Summary

  13. What is NLG? • NLG systems are computer systems which produces understandable and appropriate texts in English or other human languages • Input is data (raw, analysed) • Output is documents, reports, explanations, help messages, and other kinds of texts • Requires • Knowledge of language • Knowledge of the domain

  14. Natural Language Understanding Natural Language Generation Speech Recognition Speech Synthesis Language Technology Meaning Text Text Speech Speech

  15. Aberdeen NLG Systems • STOP (smoking cessation letters) • SumTime (weather forecasts) • Ilex (museum description) • SkillSum (feedback on assessment) • StandUp (help children make puns) • BabyTalk (summary of patient data)

  16. How do NLG Systems Work? • Usually three stages • Not including data analysis (eg segmentation)! • Document planning: decide on content and structure of text • Microplanning: decide how to linguistically express text (which words, sentences, etc to use) • Realisation: actually produce text, conforming to rules of grammar

  17. Scuba: example input • Input: result of data interpretation • Trends: segmented data (as in pract 2) • Patterns: eg, rapid ascent, sawtooth, reverse dive profile, etc (as in pract 2)

  18. Scuba: example output • Your first ascent was a bit rapid; you ascended from 33m to the surface in 5 minutes, you should have taken more time to make this ascent. You also did not stop at 5m, we recommend that anyone diving beneath 12m should stop for 3 minutes at 5m. Your second ascent was fine.

  19. Document Planning • Content selection: Of the zillions of things I could say, which should I say? • Depends on what is important • Also depends on what is easy to say • Structure: How should I organise this content as a text? • What order do I say things in? • Rhetorical structure?

  20. Scuba: content • Probably focus on patterns indicating dangerous activities • Most important thing to mention • How much should we say about these? • Detail? Explanations? • Should we say anything for safe dives? • Maybe just acknowledge them?

  21. Scuba: structure • Order by time (first event first) • Or should we mention the most dangerous patterns first? • Linking words (cue phrases) • Also, but, because, …

  22. Microplanning • Lexical choice: Which words to use? • Aggregation: How should information be distributed across sentences and paras • Reference: How should the text refer to objects and entities?

  23. SCUBA: microplanning • Lexical choice: • A bit rapid vs too fast vs unwise vs … • Ascended vs rose vs rose to surface vs … • Aggregation: 1 sentence or 3 sent? • “Your first ascent was a bit rapid; you ascended from 33m to the surface in 5 minutes, it would have been better if you had taken more time to make this ascent.”

  24. Scuba: Microplanning • Aggregation (continued) • Phrase merging • “Your first ascent was fine. Your second ascent was fine” vs • “Your first and second ascents were fine.” • Reference • Your ascent vs • Your first ascent vs • Your ascent from 33m at 3 min

  25. Realisation • Grammars (linguistic): Form legal English sentences based on decisions made in previous stages • Obey sublanguage, genre constraints • Structure: Form legal HTML, RTF, or whatever output format is desired

  26. Scuba: Realisation • Simple linguistic processing • Capitalise first word of sentence • Subject-verb agreement • Your first ascent was fine • Your first and second ascents were fine • Structure • Inserting line breaks in text (pouring) • Add HTML markups, eg, <P>

  27. Multimodal NLG • Speech output • Text and visualisations • Produce separately, OR • Tight integration • Eg, text refers to graphic, OR • graphs has text annotations • Prelim study suggest users prefer this for scuba reports (Sripada and Gao, 2007)

  28. Scuba - graph

  29. Scuba – NLG text • Risky dive with some minor problems. Because your bottom time of 12.0min exceeds no-stop limit by 4.0min this dive is risky. But you performed the ascent well. Your buoyancy control in the bottom zone was poor as indicated by ‘saw tooth’ patterns.

  30. Combined (Preferred) Risky dive with some minor problems. Because your bottom time of 12.0min exceeds no-stop limit by 4.0min this dive is risky. But you performed the ascent well. Your buoyanc control in the bottom zone was poor as indicated by ‘saw tooth’ patterns marked ‘A’ on the depth-time profile.

  31. Building NLG Systems • Knowledge • Representations • Algorithms • Systems

  32. Building NLG Systems: Knowledge • Need knowledge • Which patterns most important? • What order to use? • Which words to use? • When to merge phrases? • How to form plurals • Etc • Where does this come from?

  33. Knowledge Sources • Imitate a corpus of human-written texts • Most straightforward, will focus on • Ask domain experts • Useful, but experts often not very good at explaining what they are doing • Experiments with users • Very nice in principle, but a lot of work

  34. Scuba: Corpus • See which patterns humans mention in the corpus, and have the system mention these • See the order used by humans, and have the system imitate these • etc

  35. Building NLG Systems: Algorithms and Representations • Various algorithms and representations have been designed for NLG tasks • Will discuss in later lectures • But often can simply code NLG systems straightforwardly in Java, without special algorithms • Knowledge is more important

  36. Building NLG Systems: Systems • Ideally should be able to plug knowledge into downloadable systems • Unfortunately very little in the way of downloadable NLG systems • Mostly specialised stuff primarily of interest to academics, eg http://openccg.sourceforge.net/ • I would like to improve situation

  37. Simplenlg package • Java class library • Currently handles realisation, some parts of microplanning • Not document planning • Coverage should expand over time…

  38. Aberdeen NLG group • 20 academic staff, researchers, PhD students • Leader: Prof Chris Mellish • (one of) the best NLG groups in world • Always looking fot good PhD students…