Natural Language Inference with External Knowledge

By Qian Chen, Xiao-Dan Zhu, Zhenhua Ling, Diana Inkpen, Si Wei. 2017

43 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (2018, methodology): “…representation: As for question side, since it is generally shorter in length and could be adequately represented with less information, we follow the question encoding method used in (Chen et al., 2017a) and adopt a linear transformation to encode the question representation to a single vector.”
The Natural Language Decathlon: Multitask Learning as Question Answering (2018, background): “The most successful approaches make use of attentional models that match and align words in the premise to those in the hypothesis (Tay et al., 2017; Peters et al., 2018; Ghaeini et al., 2018; Chen et al., 2017; Wang et al., 2017b; McCann et al., 2017), but recent non-attentional models designed to ”
Annotation Artifacts in Natural Language Inference Data (2018, methodology): “We ran this experiment on three high-performing NLI models: the De-composable Attention Model (DAM; Parikh et al., 2016), 5 the Enhanced Sequential Inference Model (ESIM; Chen et al., 2017), 6 and the Densely Interactive Inference Network (DIIN; Gong et al., 2018).”
e-SNLI: Natural Language Inference with Natural Language Explanations (2018, background): “Glockner et al. show that models achieving high accuracies on SNLI, such as [21, 22, 6], show dramatically reduced performance on this simpler dataset, while the model of Chen et al. [5] is more robust due to incorporating exter-nal knowledge.”
Learning Semantic Textual Similarity from Conversations (2018, methodology): “For comparison, we also list the results of Gumbel TreeLSTM (Williams et al., 2017) which is the best sentence encoder based model, and KIM Ensemble (Chen et al., 2017) which is the current state-of-the-art.”
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (2018, methodology): “2421 or the model-specific approach (Sha et al., 2016; Chen et al., 2018), we use this knowledge to generate task-specific examples.”
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task (2018, methodology): “To address the combination of external knowledge and the neural network model, Chen (Chen et al., 2017) added extra relation informations of word pair into the encoder.”
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information (2018, methodology): “(Chen et al. 2017a; 2017b) used syntactic parse trees or lexical databases like WordNet to measure the semantic relationship among the words and Pavlick et al.”
Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020, methodology): “Some previous models are developed on the of-ﬁcial baseline (Thorne et al., 2018) with three step pipeline (Chen et al., 2017a) for fact veriﬁcation (Hanselowski et al., 2018; Yoneda et al., 2018; Yin and Roth, 2018; Nie et al., 2019).”