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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Controllable Natural Language Generation with Contrastive Prefixes ... Qian, Jing; Dong, Li; Shen, Yelong. - : arXiv, 2022 BASE Show details 2 Poolingformer: Long Document Modeling with Pooling Attention ... Zhang, Hang; Gong, Yeyun; Shen, Yelong. - : arXiv, 2021 BASE Show details 3 Generation-Augmented Retrieval for Open-Domain Question Answering ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chen, Weizhu; Gao, Jianfeng; Han, Jiawei; He, Pengcheng; Liu, Xiaodong; Mao, Yuning; Shen, Yelong. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.316 Abstract: We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/mdn7-wx22 https://underline.io/lecture/25676-generation-augmented-retrieval-for-open-domain-question-answering BASE Hide details 4 UnitedQA: A Hybrid Approach for Open Domain Question Answering ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chen, Weizhu; Cheng, Hao. - : Underline Science Inc., 2021 BASE Show details 5 Reader-Guided Passage Reranking for Open-Domain Question Answering ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chen, Weizhu; Gao, Jianfeng. - : Underline Science Inc., 2021 BASE Show details 6 Unsupervised Deep Structured Semantic Models for Commonsense Reasoning ... Wang, Shuohang; Zhang, Sheng; Shen, Yelong. - : arXiv, 2019 BASE Show details 7 Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension ... Xu, Yichong; Liu, Xiaodong; Shen, Yelong. - : arXiv, 2018 BASE Show details

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