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EmailSum: Abstractive Email Thread Summarization ...
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Generation-Augmented Retrieval for Open-Domain Question Answering ...
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
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GO FIGURE: A Meta Evaluation of Factuality in Summarization ...
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UnitedQA: A Hybrid Approach for Open Domain Question Answering ...
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SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching ...
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Reader-Guided Passage Reranking for Open-Domain Question Answering ...
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RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems ...
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