Generation of referring expressions managing structural ambiguities
This presentation is the property of its rightful owner.
Sponsored Links
1 / 24

Generation of Referring Expressions: Managing Structural Ambiguities PowerPoint PPT Presentation


  • 97 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Generation of Referring Expressions: Managing Structural Ambiguities

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Generation of referring expressions managing structural ambiguities

Generation of Referring Expressions: Managing Structural Ambiguities

I.H. KhanG. Ritchie K. van Deemter

University of Aberdeen, UK


Generation of referring expressions managing structural ambiguities

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.


Natural language generation nlg

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]


Generation of referring expressions gre

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


Gre an example

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)


The problem

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?


Our approach

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


Pattern the adj n 1 and n 2

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


Experiment 1

Experiment 1

Please, remove the roaring lions and horses.


Experiment 1 results

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


Experiment 2

Experiment 2

Please, remove the figure containing the young lions and horses.


Experiment 2 cont

Experiment 2 (cont.)

  • Results: Both hypotheses are confirmed

Please, remove the figure containing the barking dogs and cats.


Generation of referring expressions managing structural ambiguities

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


Clarity brevity trade off

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


The hypotheses readers preferences

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


Experiment 3 ns case

Experiment 3: NS Case

  • Which phrase works best to identify the filled area?

  • The barking dogs and cats

  • The barking dogs and the cats


Experiment 3 ws case

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


Experiment 3 results

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


Summary of empirical evidence

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


Algorithm development

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


Transformation rules

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


Select optimal phrase

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.


Conclusions

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


Thank you

THANK YOU


  • Login