CS4025: Pragmatics. Resolving referring Expressions Interpreting intention in dialogue Conversational Implicature

Transcription

1 CS4025: Pragmatics Resolving referring Expressions Interpreting intention in dialogue Conversational Implicature For more info: J&M, chap 18,19 in 1 st ed; 21,24 in 2 nd Computing Science, University of Aberdeen 1

2 Definition Pragmatic processing adjusts a meaning in light of the current context Look at complete monologues/dialogues, not individual sentences. Look at general context (ie, user s task, location, background) Even fuzzier than semantics... So people often study individual language phenomena and the pragmatic processing that is needed to resolve them Computing Science, University of Aberdeen 2

3 Where is Pragmatics needed? Pronouns link questions» How many pregnant women smoke?» Who are they? Referent resolution What is the cheapest hotel in Aberdeen? From.uk address (Aberdeen, Scotland) Prev question about hotels in Maryland (Aberdeen, Maryland, USA) No context (Aberdeen Harbour, Hong Kong) Computing Science, University of Aberdeen 3

4 Example Pragmatic Issues Resolving referring expressions» John saw him Resolving quantifier scope» Every manager chose an employee for the prize Resolving intention in dialogue» Can you pass the salt? Conversational implicature» Mrs. Jones made some sounds which approximated the score of Home Sweet Home. Computing Science, University of Aberdeen 4

5 Interpreting Referring Expressions How do we interpret NPs like» I saw him» I passed the course» I d like the red one» I disagree with what you just said Language contains many references to entities mentioned in previous sentences, how do we interpret these?» ie, figure out who him refers to Computing Science, University of Aberdeen 5

6 Pronouns Pronouns are him, it, they, etc I, we, you, us refer to the speaker or hearer» Usually trivial to determine referent him, it, etc refer to entities that have been previously mentioned or are otherwise salient.» Non-trivial to resolve referent Computing Science, University of Aberdeen 6

7 Simple Algorithm Last object mentioned (correct gender)» John ate an apple. He was hungry. He refers to John ( apple is not a he ) Selectional restrictions» John ate an apple in the store. It was delicious. [stores cannot be delicious] It was quiet. [apples cannot be quiet] These achieve 90% accuracy for many genres Computing Science, University of Aberdeen 7

8 Complications Some pronouns don t refer to anything» It rained must check if verb has a dummy subject Evaluate last object mentioned using parse tree, not literal text position» I went to the Tescos which is opposite Marks and Spencers.» It is a big store. [Tescos, not M&S] Computing Science, University of Aberdeen 8

9 Focus Focus John is a good student He goes to all his practicals He helped Sam with CS4001 He wants to do a project for Prof. Gray He refers to John (not Sam) Because John is the focus of the monologue Attempt to model how focus can move throughout a text Computing Science, University of Aberdeen 9

10 Salience Model how salient previously mentioned objects are to reader» Referent will be most salient object which meets constraints??» Lappin&Leass algorithm (see J&M) This is more or less the current state of the art (with some extra bits, gets 86% of referents right in some genres) Computing Science, University of Aberdeen 10

11 Need for World Knowledge» The police prohibited the fascists from demonstrating because they feared violence. vs» The police prohibited the fascists from demonstrating because they advocated violence. Exactly the same syntax, world knowledge about feared violence vs advocated violence explains. Not (yet) possible in a computer NL system Computing Science, University of Aberdeen 11

12 Interpreting Definite Noun Phrases Definite NP: A the noun phrase A rich doctor met a tax lawyer for lunch. The doctor was unhappy. The lawyer was greedy Use where pronoun is impossible or ambiguous He was unhappy He was greedy (the doctor or the lawyer?) (same as ref of first he) can refer back several paragraphs, while a pronoun can only refer back 1-2 sentences Computing Science, University of Aberdeen 12

13 Resolving Definite NPs Usually find most recently mentioned entity that fits the definite NP» The doctor matches a rich doctor, but not a tax lawyer Can also refer to parts of a previously mentioned object I bought a used car. The tires were in pretty good shape. Can also refer using a word with similar meaning» He bought a car and a new washing machine. The vehicle never worked. Computing Science, University of Aberdeen 13

14 Other Kinds of Reference Indefinite NP: An a noun phrase» A rich doctor met a tax lawyer for lunch. Introduce new instances into the conversation, without specifying their exact identity. Discourse references: refer specifically to the previous dialogue» I disagree with what you just said One-anaphora: refer by properties» I d like to buy the red one Computing Science, University of Aberdeen 14

15 Names Resolving partial names» Aberdeen Salience model» high - mentioned in conversation» medium - physical context (eg, location)» low - statistically most common referent Find highest salience match Computing Science, University of Aberdeen 15

16 Interpreting Intention in Dialogue Sentences often should not be interpreted literally» Do you know the time? 3:15 Yes OK, let s finish the meeting How decide what the right response is? Computing Science, University of Aberdeen 16

17 Many Ways of Requesting List the smokers Who smokes Please tell me who smokes Do you know who smokes I need a list of smokers I d like to find out who smokes Computing Science, University of Aberdeen 17

18 Dialogue moves (DAMSL) Being precise about what moves there are: Statement information request directive (get hearer to do something) convention (eg, Hello ) agreement information response understanding (eg, clarification request) Relate to the speech acts originally defined by philosophers such as Austin and Searle Computing Science, University of Aberdeen 18

19 Dialogue moves I m having problems with the practical» Statement - lecturer should make a note of this, perhaps make practical easier next year» Directive - lecturer should help student with the practical» Information request - lecturer should give student the solution Computing Science, University of Aberdeen 19

20 Determining dialogue move Look at Sentence itself Previous dialogue - often use finite-state models of dialogue moves» based on statistical analysis of a corpus» A bit like POS tagging each sentence gets a move tag plan recognition - analyse (using deep AI) what the speaker is attempting to do Computing Science, University of Aberdeen 20

21 Plan Recognition AI Plan Recognition algorithms match an observed set of actions to a library of known plans, and deduce which (set of) plans a person is executing.» Complete-practical, complain, show-off,... Eg, match user s actions to the Complete-practical plan. Not yet possible in real-world systems. Computing Science, University of Aberdeen 21

22 Probabilistic Dialogue Models opening suggest constrain accept.22 reject.46 closing Computing Science, University of Aberdeen 22

23 Conversational Implicature Speakers obey the following maxims (Grice 1975)» Quality: they are truthful» Quantity: they say enough, but not too much» Relevance: what they say is relevant to the conversation» Manner: they are succinct, unambiguous, and avoid obscurity Hearers make inferences based on this Computing Science, University of Aberdeen 23

24 Example: Relevance A: Where s Bill? B: There s a yellow VW outside Sue s house. A assumes B s response must be relevant (Bill has a yellow VW?) A assumes B does not know for sure where Bill is Computing Science, University of Aberdeen 24

25 Example: Manner A: Mrs. Jones made some sounds which approximated the score of Home Sweet Home. B assumes that whatever Mrs Jone did could not truthfully be described as singing Computing Science, University of Aberdeen 25

26 Example: Quantity A: Use the PC with a 20-inch monitor to run Visual C++ [there is only one PC in the room] with a 20-inch monitor is not needed to identify the PC. B assumes this is important for other reasons (VC++ requires a large screen?) Computing Science, University of Aberdeen 26

27 Summary Context affects how sentences should be intepreted.» How referring expressions are resolved» Whether a statement is interpreted literally or not» What inferences a hearer makes No universal theories» Some good algorithms for specific tasks General pragmatic processing is very knowledgeintensive and so is generally avoided (except possibly in very limited domains) Computing Science, University of Aberdeen 27