Semanlink - Open Domain Question Answering AND Arxiv Doc

[2305.06897] AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages

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2023-05-15 About

[2009.13013] SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

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2023-04-06 About

[2112.07708] Learning to Retrieve Passages without Supervision

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2022-08-28 About

[2012.15156] A Memory Efficient Baseline for Open Domain Question Answering

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2022-08-08 About

[2206.10658] Questions Are All You Need to Train a Dense Passage Retriever

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> **approach for training dense retrieval models that does not require any labeled training data**. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples.
>
> ART, in contrast, only requires access to unpaired inputs and outputs (e.g. questions and potential answer documents).
>
> It uses a new document-retrieval autoencoding scheme, where
> 1. an input question is used to retrieve a set of evidence documents, and
> 2. the documents are then used to compute the probability of reconstructing the original question.
>
> Training for retrieval based on question reconstruction enables effective unsupervised learning of both document and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning.

[Tweet](doc:2022/07/devendra_singh_sachan_sur_twitt)

> Given an
input question, ART first retrieves a small set
of possible evidences documents. It then recon
structs
the original question by attending to these
documents
>
> The
key idea in ART is to consider the retrieved documents
as a noisy representation of the original
question and question reconstruction probability
as a way of denoising that provides soft-labels for
how likely each document is to have been the correct
result

Refers to [[IZACARD 2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering](doc:2020/12/2012_04584_distilling_knowled)

2022-07-06 About

[2012.12624] Learning Dense Representations of Phrases at Scale

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2022-05-11 About

[1906.00300] Latent Retrieval for Weakly Supervised Open Domain Question Answering

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2022-01-11 About

[2102.07043] Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

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> a method for constructing **a virtual KB (VKB) trained entirely from text**

Open Predicate Query Language (OPQL): constructing a virtual knowledge base (VKB) that supports KB reasoning & open-domain QA, tackling the incompleteness of knowledge bases by constructing a virtual KB only from text

> OPQL constructs
a VKB by **encoding and indexing a set of
relation mentions** in a way that naturally enables
reasoning and can be trained without any structured
supervision.

> can be used
as an **external memory integrated into a language
model**

cf. this earlier paper [[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base](doc:2020/07/2002_10640_differentiable_rea). But does not require an initial structured KB for distant
supervision.

> The key idea in constructing the OPQL VKB is to use a
dual-encoder pre-training process, similar to 
[[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning](doc:2021/05/1906_03158_matching_the_blank)

Related work section refers to [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced). Also refers to [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_) (some authors in common)

2021-06-20 About

[2106.00882] Efficient Passage Retrieval with Hashing for Open-domain Question Answering

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2021-06-03 About

[2004.04906] Dense Passage Retrieval for Open-Domain Question Answering

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2021-06-03 About

[2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering

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> a method to train an information retrieval module for downstream tasks, **without using pairs of queries and documents as annotations**.

Uses two models (standard pipeline for open-domain QA):

- 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

[1809.00782] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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2018-09-06 About