Anaphora resolution module

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Here you will find the documentation for the Anaphora Resolution module created during the Google Summer of Code 2019. (Proposal)


What is Anaphora Resolution?

Anaphora Resolution is the problem of resolving references to earlier items in discourse.

Anaphor: A linguistic unit that refers to an earlier linguistic unit in discourse.
Antecedent: The linguistic unit that the anaphor refers to.

The most common form of this is Pronominal Anaphora, where the anaphor is a pronoun and the antecedent is a noun.

For example,

Jessica is in the sixth grade and this is her father.

Here, "her" is the anaphor, and its antecedent is "Jessica".

Anaphora Resolution in Machine Translation

Anaphora Resolution is required in Machine Translation to produce correct and fluent translations. Since different languages encode information differently, resolving the antecedent of the anaphors in text becomes essential in several language pairs.

For example,

  • Spanish -> English
La chica comió su manzana
Translation: The girl ate his/her/its apple
Resolved Anaphora: The girl ate her apple
  • Add more examples

Anaphora Resolution in Apertium

Anaphora Resolution happens in two stages in the pipeline: In the Anaphora Resolution module and the in the Transfer stage.

We find the antecedent and attach it to the anaphor in the Anaphora Resolution module and select the correct pronoun in the Transfer stage.

Anaphora Resolution Module

In the Apertium pipeline, Anaphora Resolution happens after the Lexical Selection module, right before Transfer.

The output from the Lexical Selection module is analysed, and for each anaphor, the context is processed and the perceived antecedent is attached to the Lexical Unit of the anaphor. It is then sent to Transfer.

If the input sentence is Els grups del Parlament han mostrat aquest dimarts el seu suport al batle d'Alaró

The input to the Anaphora Resolution Module is:

^El<det><def><m><pl>/The<det><def><m><pl>$ ^grup<n><m><pl>/group<n><pl>$ ^de<pr>/of<pr>/from<pr>$ 
^el<det><def><m><sg>/the<det><def><m><sg>$ ^Parlament<n><m><sg>/Parliament<n><sg>$ 
^aquest<det><dem><m><sg>/this<det><dem><m><sg>$ ^dimarts<n><m><sp>/Tuesday<n><ND>$ 
^el seu<det><pos><m><sg>/his<det><pos><m><sg>$ ^suport<n><m><sg>/support<n><sg>$ 
^a<pr>/at<pr>/in<pr>/to<pr>$ ^el<det><def><m><sg>/the<det><def><m><sg>$ 
^*batle/*batle$ ^de<pr>/of<pr>/from<pr>$ ^*Alaró/*Alaró$^.<sent>/.<sent>$

The output is as follows:

^El<det><def><m><pl>/The<det><def><m><pl>/$ ^grup<n><m><pl>/group<n><pl>/$ ^de<pr>/of<pr>/from<pr>/$ 
^el<det><def><m><sg>/the<det><def><m><sg>/$ ^Parlament<n><m><sg>/Parliament<n><sg>/$ 
^aquest<det><dem><m><sg>/this<det><dem><m><sg>/$ ^dimarts<n><m><sp>/Tuesday<n><ND>/$ 
^el seu<det><pos><m><sg>/his<det><pos><m><sg>/group<n><pl>$ ^suport<n><m><sg>/support<n><sg>/$ 
^a<pr>/at<pr>/in<pr>/to<pr>/$ ^el<det><def><m><sg>/the<det><def><m><sg>/$ 
^*batle/*batle/$ ^de<pr>/of<pr>/from<pr>/$ ^*Alaró/*Alaró/$^.<sent>/.<sent>/$

So we can see that the anaphor el seu (a possessive determiner)

^el seu<det><pos><m><sg>/his<det><pos><m><sg>$

gets modified to

^el seu<det><pos><m><sg>/his<det><pos><m><sg>/group<n><pl>$

as we attach its antecedent group to it.

This is then passed on to Transfer.


Since originally Apertium didn't deal with Anaphora Resolution, it used to put a default translation - "his" in the above example.

Now, the Anaphora Resolution Module attaches its antecedent in the LU, which we can use to change it to the correct anaphor using a macro in the transfer rules of the language pair. (t1x)

These rules represent logic similar to:

  • if antecedent is plural, change his to their.
  • if antecedent is female, change his to her.

How does it work?

Anaphora Resolution is usually done either using Parse Trees, or using Machine Learning. However, to obtain accurate Parse Trees or accurate results from an ML algorithm, one needs a lot of data.

However, Apertium is a system which deals largely with Low Resource languages and hence parse trees aren't available during translation and the language pairs usually don't have enough parallel data to train ML algorithms that give accurate results.

The Algorithm we use to resolve Anaphora in this module is a method which doesn't use parse trees or any data to train. It uses saliency scores to select an antecedent in the context.

Mitkov's Antecedent Indicators

In this algorithm, every time we encounter an anaphor, we collect a list of all possible antecedents in the current sentence and the last 3 sentences.

Then using some indicators, we give each potential antecedent a positive or a negative score. These indicators are chosen based on a knowledge of the language pair and statistical analysis.

Some of these indicators could be language pair specific and hence it is completely customisable, using the .arx files.

Here are some common indicators:

Boosting Indicators (given a positive score)

  • First NPs
  • Referential Distance: Potential antecedents closer to the anaphor are given are more likely to be the antecedent.

Impeding Indicators (given a negative score)

  • Indefiniteness: Indefinite NPs are penalised
  • Prepositional NPs: NPs which are part of a PP are penalised.

After this is done, the highest scored potential antecedent is chosen as the final antecedent and attached to the anaphor.

Reference : Multilingual Anaphora Resolution, Ruslan Mitkov

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