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Found 5 papers, 0 papers with code
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings.
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence.
Ranked #61 on
Question Answering
on HotpotQA
Multi-style Generative Reading Comprehension
Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved.
Ranked #1 on
Question Answering
on MS MARCO
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages.
