Papers with Machine Reading
Proceedings of the 2nd Workshop on Machine Reading for Question Answering (D19-58)
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| Challenge: | a workshop focuses on machine reading for question answering . despite recent progress, there is much to be desired about these datasets and systems . |
| Approach: | This year, they present a shared task on machine reading for question answering . they adapt and unified 18 distinct question answering datasets into the same format . |
| Outcome: | The proposed system achieves an average F1 score of 72.5 on the held-out datasets. |
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)
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| Challenge: | MRQA datasets have been used to benchmark progress in general-purpose language understanding. |
| Approach: | They propose to combine 18 question answering datasets into one shared task to evaluate their generalization capabilities. |
| Outcome: | The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than baseline based on BERT. |
Jack the Reader – A Machine Reading Framework (P18-4)
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Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
| Challenge: | Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. |
| Approach: | They propose a framework for Machine Reading that allows for quick prototyping by component reuse and evaluation of new models on existing datasets. |
| Outcome: | The proposed framework supports question answering, natural language inference and link prediction tasks. |
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)
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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang
| Challenge: | MRC requires machines to understand text and answer questions about the text. |
| Approach: | They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. |
| Outcome: | The proposed system is ranked at top 1 of all participants in terms of averaged F1 score. |
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale (D19-1)
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| Challenge: | Recent advances in representation learning have separated progress in both IR and MC . few studies have examined the relationship between retrieval and comprehension at different levels of granularity for development of MRS systems. |
| Approach: | They propose a simple yet effective pipeline system with consideration on hierarchical semantic retrieval at both paragraph and sentence level and their potential effects on the downstream task. |
| Outcome: | The proposed system achieves state-of-the-art on the leaderboard test sets of both FEVER and HOTPOTQA. |
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora (2020.lrec-1)
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| Challenge: | a recent study has shown that language mismatch and domain mismatch can affect performance of a machine reading task . a factor between language mismatched and domain-mismatched has the strongest influence on performance . |
| Approach: | They compare the cross-language and cross-domain capabilities of BERT on a machine reading comprehension task on two corpora: SQuAD and a new French Machine Reading dataset. |
| Outcome: | The proposed model matches human performance on a machine reading comprehension task with BERT on Chinese and French documents with interesting results. |