Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition

By Lei Sha, Baobao Chang, Zhifang Sui, Sujian Li. 2016

68 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Learned in Translation: Contextualized Word Vectors (2017, neutral): “…90.8 NTI [Munkhdalai and Yu, 2016b] 53 [Dai and Le, 2015] 92.8 NTI [Munkhdalai and Yu, 2016b] 87.3 bmLSTM [Radford et al., 2017] 92.9 re-read LSTM [Sha et al., 2016] 87.5 TRNN [Dieng et al., 2016] 93.8 btree-LSTM [Paria et al., 2016] 87.6 oh-LSTM [Johnson and Zhang, 2016] 94.1 600D ESIM [Chen et…”
Enhanced LSTM for Natural Language Inference (2016, background): “Sha et al. (2016) proposes a special LSTM variant which considers the attention vector of another sentence as an inner state of LSTM. Paria et al. (2016) use a neural architecture with a complete binary tree-LSTM encoders without syntactic information.”
Natural Language Inference over Interaction Space (2017, methodology): “(ii) Joint feature models which use the cross sentence feature or attention from one sentence to another(Rockt¨aschel et al., 2015; Wang & Jiang, 2015; Cheng et al., 2016; Parikh et al., 2016; Wang et al., 2017; Yu & Munkhdalai, 2017; Sha et al., 2016).”
Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment (2018, methodology): “…important, because the SNLI corpus is widely used as training data by many NN models for RTE (Bowman et al., 2015; Rockt¨aschel et al., 2015; Yin et al., 2016; Mou et al., 2016a; Wang and Jiang, 2016; Liu et al., 2016a; Liu et al., 2016b; Cheng et al., 2016; Parikh et al., 2016; Sha et al., 2016).”
Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference (2016, background): “The original SNLI corpus contains also “ the other ” category, which includes the sentence pairs lacking consensus among multiple human annotators.”
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (2018, background): “Various deep learning models have been proposed that achieve successful results for this task (Gong et al., 2017; Wang et al., 2017; Chen et al., 2017; Yu and Munkhdalai, 2017a; Parikh et al., 2016; Zhao et al., 2016; Sha et al., 2016).”
Convolutional Interaction Network for Natural Language Inference (2018, unknown): “Rockt¨aschel et al. (2015) ﬁrstly use LSTM with attention for text matching task.”
Neural Natural Language Inference Models Enhanced with External Knowledge (2017, unknown): “To represent words in its context, the premise and the hypothesis are fed into BiLSTM encoders (Hochreiter and Schmidhuber, 1997) to obtain context-dependent hidden states a s and b s : where i and j indicate the i -th word in the premise and the j -th word in the hypothesis, respectively.”
A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference (2017, result): “Though we compare with many other models (Wang and Jiang, 2016b; Rocktäschel et al., 2015; Parikh et al., 2016; Yu and Munkhdalai, 2017; Sha et al., 2016), the key SoTA competitors on this dataset are the BiMPM (Wang et al.”
Natural Language Inference with External Knowledge (2017, background): “…(Wang & Jiang, 2016) 86.1 LSTMN (Cheng et al., 2016) 86.3 Decomposable Att (Parikh et al., 2016) 86.8 NTI (Munkhdalai & Yu, 2016b) 87.3 Re-read LSTM (Sha et al., 2016) 87.5 BiMPM (Wang et al., 2017) 87.5 btree-LSTM (Paria et al., 2016) 87.6 DIM (Gong et al., 2017) 88.0 ESIM (Chen et al., 2017a)…”
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (2019, background): “…have made it feasible to train complicated neural models which have achieved the state-of-the-art results (Bowman et al., 2015; Parikh et al., 2016; Sha et al., 2016; Chen et al., 2017b,c; Munkhdalai and Yu, 2017; Nie and Bansal, 2017; Conneau et al., 2017; Gong et al., 2018; Tay et al., 2018;…”