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Generation of Referring Expressions: Managing Structural Ambiguities

Generation of Referring Expressions: Managing Structural Ambiguities. I.H. Khan G. Ritchie K. van Deemter University of Aberdeen, UK.

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Generation of Referring Expressions: Managing Structural Ambiguities

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  1. Generation of Referring Expressions: Managing Structural Ambiguities I.H. Khan G. Ritchie K. van Deemter University of Aberdeen, UK

  2. A natural language generator should avoid generating those phrases, which are too ambiguous to understand. But, how the generator can know whether a phrase is too ambiguous or not? We use corpus-based heuristics, backed by empirical evidence, that estimate the likelihood of different readings of a phrase, and guide the generator to choose an optimal phrase from the available alternatives.

  3. Natural Language Generation (NLG) • Process of generating text in natural language (e.g., English) from some non-linguistic data (Reiter & Dale, 2000) • Example NLG system • Pollen Forecast: generates reports from pollen forecast data Grass pollen levels for Tuesday have decreased from the high levels of yesterday with values of around 4 to 5 across most parts of the country. However, in South Eastern areas, pollen levels will be high with values of 6. [courtesy E. Reiter]

  4. Generation of Referring Expressions (GRE) • Referring Expression = Noun Phrase • e.g., the black cat; the black cats and dogs (etc.) • A key component in most NLG systems • Task of GRE: • Given a set of intended referents, compute the properties of these referents that distinguish them from distractors in a KB

  5. GRE: An Example • Input:KB, Intended Referents R • Task: find properties that distinguish R from distractors KB • Output: Distinguishing Description (DD) • (Black  Sheep)  (Black  Goat)

  6. NP1: The black sheep and the black goats = {Object1,Object3,Object4,Object6} (Black  Sheep)  (Black  Goat) NP2: The black sheep and goats (Black  Sheep)  Goat = {Object1,Object3,Object4,Object5,Object6,Object7} The Problem • Linguistic ambiguities can arise when DDs are realised • NP1 unambiguous and long; NP2 ambiguous and brief • Question: How the generator might chose between NP1 and NP2?

  7. Our Approach • Psycholinguistic evidence • Avoidance of all ambiguity is not feasible (Abney, 1996) • Avoid only distractor interpretations • An interpretation is distractor if it is more likely or almost as likely as the intended one. • Question • How to make distractor interpretation precise? • Our solution • Getting likelihood using word sketches (cf. Chantree et el., 2004) • Word sketches provide detailed information about word relationships, based on corpus frequencies • Relationships are grammatical

  8. Pattern: the Adj N1 and N2 • Hypothesis 1 • If Adj modifies N1 more often than N2, then a narrow-scope reading is likely (no matter how frequently N1 and N2 co-occur). bearded men and women handsome men and women • Hypothesis 2 • If Adj does not modify N1 more often than N2, then a wide-scope reading is likely (no matter how frequently N1 and N2 co-occur).. old men and women tall men and trees

  9. Experiment 1 Please, remove the roaring lions and horses.

  10. Experiment 1: Results • Hypothesis 2(i.e., predictions for WS reading) is confirmed • Hypothesis 1(i.e., predictions for NS reading) is not confirmed • Tendency for WS (even though results are not stat. sig.) • Tentative conclusion • An intrinsic bias in favour of WS reading • BUT: The use of *unusual* features may have made people’s judgements unreliable

  11. Experiment 2 Please, remove the figure containing the young lions and horses.

  12. Experiment 2 (cont.) • Results: Both hypotheses are confirmed Please, remove the figure containing the barking dogs and cats.

  13. The black sheep and the black goats (Black  Sheep)  (Black  Goat) The black sheep and goats • Word Sketches can make reasonable predictions about how an NP would be understood. • But we need more to know from generation point of view: which of the following two NPs is best? (Black  Sheep)  Goat • We seek the answer in next experiment

  14. Clarity-brevity trade-off • Recall the pattern: the Adj Noun1 and Noun2 • Brief descriptions (+b) take the form • the Adj Noun1 and Noun2 • Non-brief descriptions (-b) take the form • the Adj Noun1 and the Adj Noun2 (IR = WS) • the Adj Noun1 and the Noun2 (IR = NS) • Clear descriptions (+c) • Which have no distractor interpretations • Non-clear descriptions (-c) • Which have some distractor interpretations

  15. The Hypotheses (Readers’ Preferences) • Hypothesis 1 • (+c, +b) descriptions are preferred over (+c, -b) • Hypothesis 2 • (+c, -b) descriptions are preferred over (-c, +b) • Each hypothesis is tested under two conditions • C1:intended reading is WS • C2: intended reading is NS

  16. Experiment 3: NS Case • Which phrase works best to identify the filled area? • The barking dogs and cats • The barking dogs and the cats

  17. Experiment 3: WS Case • Which phrase works best to identify the filled area? • The young lions and the young horses • The young lions and horses

  18. Experiment 3: Results • Both hypotheses are confirmed: • (+c, +b) descriptions are preferred over (+c, -b) • (+c, -b) descriptions are preferred over (-c, +b) • Role of length: • In WS cases preferences are very strong • In NS cases preference is not as strong as in WS cases

  19. Summary of Empirical Evidence • For the pattern the Adj Noun1 and Noun2 • Word Sketches can make reliable predictions • Keeping clarity the same, a brief NP is better than a longer one

  20. Algorithm Development • Main knowledge sources • WordNet (for lexicalisation) • SketchEngine (for predicting the most likely reading) • Main steps • Choose words • Use these to construct description in DNF • Use transformations to generate alternative structures from DNF • Select optimal phrase

  21. Transformation Rules • Input • Logical formula in DNF • Rule Base • (A  B1)  (A  B2)  A  (B1 B2) • (X  Y)  (Y  X) [A = Adj, B1=B2=Noun, X=Y=(Adj and/or Noun)] • Output • Set of logical formulae

  22. Select optimal phrase • (black  sheep)  (black  goats) DNF • (black  goats)  (black  sheep) • black  (goats  sheep) • black  (sheep  goats) Optimal (4):Adj has high collocational frequency with N1 and N2, so the intended (wide-scope) reading is more likely. Therefore, (4) is selected.

  23. Conclusions • GRE should deal with surface ambiguities • Word sketches can make distractor interpretation precise • Keeping clarity the same, brief descriptions are preferred over longer ones • A GRE algorithm is sketched that balances clarity and brevity

  24. THANK YOU

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