MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

By Payal Bajaj, Daniel Fernando Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Minh Nguyen and 5 more. 2016

524 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Dual Co-Matching Network for Multi-choice Reading Comprehension (2019, background): “ndamental and long-standing goal of natural language understanding which aims to teach machine to answer question automatically according to given passage (Hermann et al. 2015; Rajpurkar et al. 2016; Nguyen et al. 2016; Zhang et al. 2018). In this paper, we focus on multi-choice MRC tasks such as RA”
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, background): “gnizing the need to align research with the impact it will have on real users, the community has responded with datasets of information-seeking questions such as WikiQA (Yang et al., 2015), MS MARCO (Nguyen et al., 2016), QuAC (Choi et al., 2018), and the Natural Questions (NQ) (Kwiatkowski et al., ”
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (2019, background): “nswering (QA) has mostly focused on answering factoid questions, where the answer can be found in a given context with little need for commonsense knowledge (Hermann et al.,2015;Rajpurkar et al.,2016;Nguyen et al., 2016;Joshi et al.,2017). Small benchmarks such as the Winograd Scheme Challenge (Leve”
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions (2020, background): “n, 2017). Questionanswering, which can be thought of as a single turn task is similar, either requiring reading comprehension (Rajpurkar et al., 2016) or retrieval as well in the more realistic case (Nguyen et al., 2016; Chen et al., 2017). Although potentially any amount of reasoning is required to”
ELI5: Long Form Question Answering (2019, background): “MS MARCO (Nguyen et al., 2016), a dataset of crowdsourced responses to Bing queries, has written answers around 1 sentence long with short input passages.”
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (2020, methodology): “The figure uses MS MARCO Ranking [24], a recent collection of 9M passages and 1M queries from Bing’s logs.”