Natural Language Agents

1. Natural Language Agents Elaine Rich

2. What do they Need? The linguistic facts A KR system that is rich enough to encode everything we can talk about Understanding: Figuring out what the speaker meant and what you should do about it Generation: Figuring out what to say to achieve your goals

3. Turing’s Prediction 1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning".

4. Was Turing Right? >What's the best place to eat Chinese food? ALICE:My favorite places are California and Maine. Holland is cool too. >What do you like about Maine? ALICE:The special effects are the best.

5. Another Application - MT Austin Police are trying to find the person responsible for robbing a bank in Downtown Austin. El policía de Austin está intentando encontrar a la persona responsable de robar un banco en Austin céntrica. The police of Austin is trying to find the responsible person to rob a bank in centric Austin.

6. MT - The Limits A Florida teen charged with hiring an undercover policeman to shoot and kill his mother instructed the purported hitman not to damage the family television during the attack, police said on Thursday. Un adolescente de la Florida cargado con emplear a un policía de la cubierta interior para tirar y para matar a su madre mandó a hitman pretendida para no dañar la televisión de la familia durante el ataque, limpia dicho el jueves. An adolescent of Florida loaded with using a police of the inner cover to throw and to kill his mother commanded to hitman tried not to damage the television of the family during the attack, clean said Thursday.

7. I have a dream, that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character. I have a dream today – Martin Luther King I am a sleepy, that my four small children a day of alive in a nation in where they will not be judged by the color of its skin but by the content of its character. I am a sleepy today. (Spanish) MT - The Limits http://www.shtick.org/Translation/translation47.htm

8. Going Both Ways • Notice that both of these applications require that we process language in two directions: • Understanding • Generation • But also notice that it is possible to do a somewhat passable job without going through any meaning representation.

9. When Meaning is Critical English: Put the kid’s cereal on the bottom shelves.

10. Java import java.util.ArrayList; public class GroceryStore { private int[][][] shelves; private ArrayList products; public void placeProducts(String productFile) { FileReader r = new FileReader(productFile); GroceryItemFactory factory = new GroceryItemFactory(); while(r.hasNext()) products.add( factory.createItem(r.readNext())); ThreeDLoc startLoc; GroceryItem temp; for(itemNum = 0; itemNum < products.size(); itemNum++) { temp = (GroceryItem)(products.get(itemNum)) startLoc = temp.getPlacement(this); shelves[startLoc.getX()][startLoc.getY()][startLoc.getY()]= tempgetIDNum(); } } }

11. Java, Continued public class ChildrensCereal extends GroceryItem { private static final int PREFERRED_X = -1; private static final int PREFERRED_Y = 0; private static final int PREFERRED_Z = 0; public ThreeDLoc getPlacement(GroceryStore store) { ThreeDLoc result = new ThreeDLoc(); result.setX(store.find(this)); result.setY(PREFERRED_Y); result.setZ(PREFERRED_Z); return result; } }

12. It’s All about Mapping

13. What Are We Going to Map to? English: Do you know how much it rains in Austin? The database:

14. English: What is the average rainfall, in Austin, in months with 30 days? SQL: SELECT Avg(RainfallByStation.rainfall) AS AvgOfrainfall FROM Stations INNER JOIN (Months INNER JOIN RainfallByStation ON Months.Month = RainfallByStation.month) ON Stations.station = RainfallByStation.station HAVING (((Stations.City)="Austin") AND ((Months.Days)=30));

15. Designing a Mapping Function for NL Understanding • Morphological Analysis and POS tagging • * The womans goed home. • Syntactic Analysis (Parsing) • * Fishing went boys older • Extracting Meaning • Colorless green ideas sleep furiously. • Sue cooked. The potatoes cooked. • * Sue and the potatoes cooked. • Putting it All in Context • My cat saw a bird out the window. It batted at it. • What isn’t Said • Winnie doesn’t like August. • He doesn’t like melted ice cream.

16. Ambiguity – the Core Problem • Time flies like an arrow. • I hit the boy with the blue shirt (a bat). • I saw the Grand Canyon (a Boeing 747) • flying to New York. • I know more beautiful women than Kylie. • The boys may not come. • I only want potatoes or rice and beans. • Is there water in the fridge? • Who cares? • Have you finished writing your paper? • I’ve written the outline.

17. Morphological Analysis and POS Tagging Morphological Analysis: played = play + ed = play (V) + PAST saw = see (V) + PAST leaves =

18. Morphological Analysis and POS Tagging Morphological Analysis: played = play + ed = play (V) + PAST saw = see (V) + PAST leaves = leaf (N) + PL = leave (N) + PL = leave (V) + 3rdS compute

19. Morphological Analysis and POS Tagging Morphological Analysis: played = play + ed = play (V) + PAST saw = see (V) + PAST leaves = leaf (N) + PL = leave (N) + PL = leave (V) + 3rdS compute computer computerize computerization POS Tagging: I hit the bag.

20. Morphological Analysis Using a Finite State Transducer

21. Stochastic POS Tagging Naïve Bayes Classification: Choose the POS tag that is most likely for the current word given its context. For example: Secretariat expected to race tomorrow. Using Bayes Rule We want to choose the tag tj with maximum likelihood:

22. The Importance of Parsing Even When We’re Not Doing Full Understanding • Find me all the: • Lawyers whose clients committed fraud • vs • Lawyers who committed fraud • vs • Clients whose lawyers committed fraud

23. Parsing - Building a Tree S NP VP N V NP John hit DET N the ball John hit the ball. (S (NP (N John)) (VP (V hit) (NP (DET the) (N ball))))

24. Grammar Rules • We can build such a parse tree using a grammar with rules such as: • S  NP VP • NP  N • VP  V NP

25. The Lexicon is Important * The cat with a furry tail purred a collar. Mary imagined a cat with a furry tail. Mary decided to go. * Mary decided a cat with a furry tail. Mary decided a cat with a furry tail would be her next pet. Mary gave Lucy the food. * Mary decided Lucy the food. Mary asked the cat. Mary demanded a raise. Mary asked for a raise.

26. Parsing: Dealing with Ambiguity English: Water the flowers with the hose. Water the flowers with brown leaves.

27. Using Domain Knowledge (plant (isa living thing)) (flower (isa plant) (has parts leaf)) (water (isa action) (instrument mustbe container)) (hose (isa container))

28. A Harder One John saw a boy and a girl with a red wagon with one blue and one white wheel dragging on the ground under a tree with huge branches.

29. How Bad is the Ambiguity? • Kim (1) • Kim and Sue (1) • Kim and Sue or Lee (2) • Kim and Sue or Lee and Ann (5) • Kim and Sue or Lee and Ann or Jon (14) • Kim and Sue or Lee and Ann or Jon and Joe (42) • Kim and Sue or Lee and Ann or Jon and Joe or Zak (132) • Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel (469) • Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy (1430) • Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy and Jan (4862) • The number of parses for an expression with n terms is the n’th Catalan number:

30. Parsing: Gapping English: Who did you say Mary gave the ball to? Sentences like this make specifying the grammar difficult. They also make it hard to use a simple, context-free parser.

31. Semantics: The Meaning of Words Getting it right for the target application: “month”  RainfallByStation.month Dealing with ambiguity: “spring”  or or “stamp”  or

32. Semantics: The Meaning of Phrases Semantics is (mostly) compositional. Olive oil (oil (made-from olives))

33. Occasionally It Isn’t Olive oil

34. But Usually It Is Peanut oil (oil (made-from peanuts))

35. Another One Coconut oil (oil (made-from coconut))

36. But What About This One? Baby oil

37. But What About This One? Baby oil (oil (used-on baby))

38. And Another One Cooking oil (oil (used-for cooking))

39. And Another One Riding jacket Leather jacket Letter jacket Rain jacket

40. Idioms Don’t Work This Way • I’m going to give her a piece of my mind. • He bent over backwards to make the sale. • I’m going to brush up on my Spanish.

41. Putting Phrases Together Bill cooked the potatoes. The potatoes cooked in about an hour. The heat from the fire cooked the potatoes in 30 minutes. (cooking-event (agent ) (object ) (instrument ) (time-frame ) * Bill and the potatoes cooked.

42. Language at its Most Straightforward –Propositional Content • Bill Clinton was the 42nd president of the United States. • Texas is in France. • The Matrix is playing at the Dobie. • Lunch is at noon. • What time is it?

43. When There’s More - Presuppositions • What is Clinton famous for? • Where’s The Matrix playing? • Who is the king of France? • Have you started making it to your morning classes? • I’m going to check out all the five star restaurants in Cleveland on this trip.

44. Coherence Winnie doesn’t like melted ice cream. He always dreads August. * Winnie doesn’t like melted ice cream. He always dreads January. Winnie wanted to go to the store. He went to find Christopher Robin. * Winnie wanted to go to the store. He counted quickly to 10. Winnie walked into the room. Christopher Robin looked up and smiled. * Winnie walked into the room. The earth rotates around the sun.

45. We Can’t Say it All Christopher Robin and Winnie decided to go out for lunch. They remembered that Coji’s doesn’t have hot dogs on Saturdays, so they went to Buzzy’s. They got their food, slathered on the mustard, and walked home.

46. Conversational Postulates • Grice’s maxims: • The Maxim of Quantity: • Be as informative as required. • Don’t be more so. • The Maxim of Quality: • Do not say what you believe to be false. • Do not say that for which you lack sufficient evidence. • Maxim of relevance: Be relevant • Maxim of manner: • Avoid obscurity of expression • Avoid ambiguity • Be brief. • Be orderly.

47. Conversational Postulates and Scalar Implicature • A: Have you done the first math assignment yet? • B: I’m going to go buy the book tomorrow.

48. Another Example of Scalar Implicature • A: When did you get home last night? • B: I was in bed by midnight.

49. When There’s More – Conversational Postulates and Inference • A: Joe doesn't seem to have a girl-friend these days. • B: He's been going to Dallas a lot lately.

50. When There’s More – Conversational Postulates and Inference A: Let’s go to the movies tonight. B: I have to study for an exam.