Semanlink - Favoris ; Education AND Question Answering

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 About

[2006.09462] Selective Question Answering under Domain Shift

Tags:

2020-06-30 About

Answering Complex Open-domain Questions at Scale | SAIL Blog

Tags:

2019-10-21 About

The Stanford Question Answering Dataset

Tags:

2018-11-05 About