Open Domain Question Answering Skill on Wikipedia¶
Open Domain Question Answering (ODQA) is a task to find an exact answer to any question in Wikipedia articles. Thus, given only a question, the system outputs the best answer it can find:
:: What is the name of Darth Vader's son? >> Luke Skywalker
There are pretrained ODQA models for English and Russian languages in DeepPavlov DeepPavlov.
The architecture of ODQA skill is modular and consists of two models, a ranker and a reader. The ranker is based on DrQA  proposed by Facebook Research and the reader is based on R-NET  proposed by Microsoft Research Asia and its implementation  by Wenxuan Zhou.
Tensorflow-1.8.0 with GPU support is required to run this model.
About 16 GB of RAM required
TensorFlow 1.8 with GPU support is required to run this skill.
About 16 GB of RAM required.
When interacting, the ODQA skill returns a plain answer to the user’s question.
Run the following to interact with English ODQA:
cd deeppavlov/ python deep.py interact deeppavlov/configs/odqa/en_odqa_infer_wiki.json -d
Run the following to interact with Russian ODQA:
cd deeppavlov/ python deep.py interact deeppavlov/configs/odqa/ru_odqa_infer_wiki.json -d
Scores for ODQA skill:
|DeepPavlov||SQuAD (dev)||enwiki (2018-02-11)||28.0||22.2|
|DrQA ||SQuAD (dev)||enwiki (2016-12-21)||-||27.1|
EM stands for “exact-match accuracy”. Metrics are counted for top 5 documents returned by retrieval module.