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Heterogeneous Answering (HA)

Module to answer the intent-explicit representation of the question, using the top-e retrieved evidences.

Create your own HA module

You can inherit from the HeterogeneousAnswering class and create your own QU module. Implementing the function inference_on_turn is sufficient for the pipeline to run properly. You might want to implement your own training procedure for your module via the train function though.

Further, you need to instantiate a logger in the class, which will be used in the parent class. Alternatively, you can call the init method of the parent class.

inference_on_turn function

Inputs:

  • turn: the turn, for which the answer should be predicted. You can access the intent-explicit representation of the information need via turn["structured_representation"], and the top-e evidences via turn["top_evidences"].

Description:
Run the HA module on the information need, and predict the answer(s).

Output: Returns the turn. Make sure to add the predicted answers to turn["pred_answers"]. You can find additional information on the expected answer format below.

[Optional] train function

Inputs:

  • sources: list of sources for which the HA module should be trained. The default setting is to train a single model for all sources (and combinations of sources) for generalizability.

Description:
If required, you can train your HA module here. You can make use of whatever parameters are stored in your .yml file.

Output: NONE

Answer format

The predicted answers are given as list of answer-dictionaries, and should be stored in turn["pred_answers"]. Note, that the answers should be normalized to Wikidata. This allows for fair comparison beyond plain string matching. Further, in a real use-case this has the advantage that knowledge cards can be shown for the given KB items. In case a date or year is returned, give the corresponding timestamp as the ID ("2011-04-17T00:00:00Z"; standard format in Wikidata, and the CLOCQ API), and a verbalized version as label ("17 April 2011"). You can make use of the timestamp-related functions in the StringLibrary.

[{
	"id": "<WIKIDATA ITEM ID>",
	"label": "<WIKIDATA ITEM LABEL>",
	"rank": "<RANK INTEGER>" 
}]

rank starts with 1, and has exactly one answer at every rank (for comparison on ConvMix).

Example:

[
  {
    "id": "Q23633",
    "label": "HBO",
    "rank": "1" 
  },
  {
    "id": "2011-04-17T00:00:00Z",
    "label": "17 April 2011",
    "rank": "2" 
  }
]