Reading Twice for Natural Language Understanding

By Dirk Weissenborn. 2017

16 citations recorded by the source corpus.

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Citations in the corpus

Efficient and Robust Question Answering from Minimal Context over Documents (2018, background): “, 2018), Reading Twice for Natural Language Understanding (Weissenborn, 2017) and Mnemonic Reader (Hu et al.”
A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference (2017, methodology): “To ascertain the effectiveness of our models, we use the SNLI (Bowman et al., 2015) and MultiNLI (Williams et al., 2017) benchmarks which are standard and highly competitive benchmarks for the NLI task.”
Using Natural Language Relations between Answer Choices for Machine Comprehension (2019, background): “, 2018) or an external commonsense knowledge base (Das et al., 2017; Mihaylov and Frank, 2018; Bauer et al., 2018; Weissenborn et al., 2017).”
Densely Connected Attention Propagation for Reading Comprehension (2018, unknown): “, 2017], R2-BiLSTM [Weissenborn, 2017] and AMANDA [Kundu and Ng, 2018].”
Memory, Show the Way: Memory Based Few Shot Word Representation Learning (2018, background): “Hill et al. (2016) learn an embedding of a dictionary deﬁnition to match the pre-trained headword vector, while Weissenborn (2017) re-ﬁnes the word embeddings with explicit background knowledge from a commonsense knowledge base.”
Retrospective Reader for Machine Reading Comprehension (2020, unknown): “very high Model Dev Test EM F1 EM F1 Existing Systems BARB (Trischler et al. 2017) 36.1 49.6 34.1 48.2 mLSTM (Wang and Jiang 2017) 34.4 49.6 34.9 50.0 BiDAF (Seo et al. 2016) - - 37.1 52.3 R2-BiLSTM (Weissenborn 2017) - - 43.7 56.7 AMANDA (Kundu and Ng 2018) 48.4 63.3 48.4 63.7 DECAPROP (Tay et al. ”