Modeling of the Question Answering Task in the YodaQA System

By Petr Baudis, Jan Sedivý. 2015

174 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Information Retrieval & Search

In Natural Language Processing

Citations in the corpus

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020, methodology): “Our RAG models achieve state-of-the-art results on open Natural Questions [25], WebQuestions [3] and CuratedTrec [2] and strongly outperform recent approaches that use specialised pre-training objectives on TriviaQA [20].”
Latent Retrieval for Weakly Supervised Open Domain Question Answering (2019, background): “CuratedTrec is a corpus of question-answer pairs derived from TREC QA data curated by Baudis and Sediv ´ y (2015).”
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval (2020, methodology): “It includes ﬁve OpenQA tasks, including Natural Questions (NQ) [26], TriviaQA [27], WebQuestions (WQ) [28], CuratedTREC [29], and SQuAD [5].”
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering (2020, methodology): “For WebQuestions and CuratedTREC, we ﬁnetune for 10 epochs.”
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (2018, methodology): “CuratedTrec (Baudiš and Šedivỳ, 2015) was created for TREC opendomain QA tasks.”
Learning to Retrieve Passages without Supervision (2021, methodology): “…used in prior work, all over Wikipedia: Natural Questions (NQ; Kwiatkowski et al. 2019), Trivi-aQA (Joshi et al., 2017), WebQuestions (WQ; Be-rant et al. 2013), CuratedTREC (TREC; Baudiš and Šedivý 2015), SQuAD (Rajpurkar et al., 2016) and EntityQuestions (EntityQs; Sciavolino et al. 2021).”
Learning Dense Representations of Phrases at Scale (2020, neutral): “For the open-domain QA experiments, we evaluate our approach on ﬁve popular open-domain QA datasets: Natural Questions, WebQuestions (WQ) (Berant et al., 2013), CuratedTREC (TREC) (Baudiš and Šediv`y, 2015), TriviaQA (TQA) (Joshi et al., 2017), and SQuAD.”
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (2019, methodology): “To reduce the length to meet our second requirement ( ≤ 800 to-kens), for each question we manually selected a TREC (Baudiˇs and ˇSediv´y, 2015) The Text REtrieval Conference (TREC) dataset is curated from the TREC QA tasks (Voorhees and Tice, 2000) from 1999-2002.”
Combining Fact Extraction and Verification with Neural Semantic Matching Networks (2018, methodology): “Chen et al. (2017a) was the first to successfully applied this framework to open domain QA, obtaining stateof-the-art results on several QA benchmarks (Rajpurkar et al. 2016; Baudiš and Šedivỳ 2015; Miller et al. 2016; Berant et al. 2013).”
Adaptive Document Retrieval for Deep Question Answering (2018, methodology): “, 2016), Curated TREC (Baudiš and Šedivý, 2015), WikiMovies (Miller et al.”
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension (2018, methodology): “As distant super-vision datasets, we can use the RC datasets without answer spans or other QA datasets without evidence passages, such as Curat-edTREC [1], WebQuestions [2], or WikiMoviews [29].”
Contextualized Sparse Representation with Rectified N-Gram Attention for Open-Domain Question Answering (2019, methodology): “CURATEDTREC consists of question-answer pairs from TREC QA (Voorhees et al., 1999) curated by Baudiš & Šedivỳ (2015).”
ReQA: An Evaluation for End-to-End Answer Retrieval Models (2019, unknown): “Chen et al. (2017) recently proposed DrQA, treating Wikipedia as a knowledge base over which to answer factoid questions from SQuAD (Rajpurkar et al., 2016), Curat-edTREC (Baudiˇs and ˇSediv´y, 2015) and other sources.”