We propose a deep architecture that jointly exploits convolutional and recurrent networks for learning domain-specific features while transferring domain-shared ...
Abstract—Corpora used to learn open-domain Question-. Answering (QA) models are typically collected from a wide variety of topics or domains.
Jul 11, 2018 · "Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed.
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To construct the adjacency matrix A, for each node pair (v i , v j ), we applied cosine similarity based on enriched TFIDF features 3 as the value A ij .
Apr 9, 2024 · In this paper, we propose a simple and novel mutual learning framework to improve the performance of retrieve-then-read-style models.
Missing: Transferable | Show results with:Transferable
May 4, 2023 · In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets.
In this paper, we study a simple approach to open domain question answering, which relies on retriev- ing support passages before processing them with a.
Mar 16, 2022 · We consider the problem of pretraining a two-stage open-domain question answering (QA) system (retriever + reader) with strong transfer capabilities.
Missing: Transferable | Show results with:Transferable
Open-domain question answering (QA), the task of answering questions using a large collection of documents of diversified topics, has been a long-.
This article will introduce the different forms of QA, the components of these 'QA stacks', and where we might use them.
Missing: Transferable | Show results with:Transferable