Mnemonic Reader for Machine Comprehension

By Minghao Hu, Yuxing Peng, Xipeng Qiu. 2017

41 citations recorded by the source corpus.

Subfields: Model Architectures, Question Answering & Reading Comprehension

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Citations in the corpus

Adversarial Examples for Evaluating Reading Comprehension Systems (2017, methodology): “…with publicly available test-time code: ReasoNet Single and Ensemble versions (Shen et al., 2017), Mnemonic Reader Single and Ensemble versions (Hu et al., 2017), Structural Embedding of Dependency Trees (SEDT) Single and Ensemble versions (Liu et al., 2017), jNet (Zhang et al., 2017),…”
Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, background): “…thereof (Peng et al., 2015; Weissenborn, 2016; Xiong et al., 2017; Liu and Perez, 2017), which is often iterated over several times (Sordoni et al., 2016; Neumann et al., 2016; Seo et al., 2017b; Hu et al., 2017) and may contain trainable stopping mechanisms (Graves, 2016; Shen et al., 2017).”
Neural Approaches to Conversational AI (2018, neutral): “Single-step reasoning is simple yet efficient and the model parameters can be trained using the classical back-propagation algorithm, thus it is adopted by most of the systems (Chen et al., 2016b; Seo et al., 2016; Wang et al., 2017b; Liu et al., 2017; Chen et al., 2017a; Weissenborn et al., 2017; H”
Stochastic Answer Networks for Machine Reading Comprehension (2017, background): “It is simple yet efﬁcient and can be trained using the classical back-propagation algorithm, thus it is adopted by most of systems (Chen et al., 2016; Seo et al., 2016; Wang et al., 2017; Liu et al., 2017; Chen et al., 2017; Weissenborn et al., 2017; Hu et al., 2017).”
Efficient and Robust Question Answering from Minimal Context over Documents (2018, background): “For TriviaQA(Wikipedia), they are Neural Casecades (Swayamdipta et al., 2018), Reading Twice for Natural Language Understanding (Weissenborn, 2017) and Mnemonic Reader (Hu et al., 2017).”
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension (2017, methodology): “Many works are based on the task of machine reading comprehension, and attention mechanism have been particularly successful(Xiong et al., 2016; Cui et al., 2016; Wang et al., 2016; Seo et al., 2016; Hu et al., 2017; Shen et al., 2016; Wang et al., 2017).”
Multi-Mention Learning for Reading Comprehension with Neural Cascades (2017, methodology): “The effect of truncation on exact match in Figure 5 (left) indicates that more scalable approaches that can take advantage of longer documents have the potential to perform better on the TriviaQA task.”
Contextualized Word Representations for Reading Comprehension (2017, neutral): “Analysis of current RC models has shown that models tend to react to simple word-matching be-tween the question and document (Jia and Liang, 2017), as well as beneﬁt from explicitly providing matching information in model inputs (Hu et al., 2017; Chen et al., 2017; Weissenborn et al., 2017).”
DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding (2017, methodology): “…dependency context-aware sequence representation, structured attention (Kim et al. 2017; Liu and Lapata 2017) and self-alignment attention (Hu, Peng, and Qiu 2017) are proposed, using cosine similarity between each pair of element form input sequence to mitigate the weakness of LSTM on…”
On Knowledge distillation from complex networks for response prediction (2019, background): “Similarly, some models (Group and Asia, 2017; Seo et al., 2016; Hu et al., 2017) contain a self-attention network which computes the importance of every document word w.r.t. every other document word.”
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions (2018, methodology): “For example, the models proposed in [ Xiong et al. , 2016; Seo et al. , 2016; Wang et al. , 2017; Hu et al. , 2017 ] address the ﬁrst variant of RC requiring span prediction as in the SQuAD dataset.”
Making Neural Machine Reading Comprehension Faster (2019, methodology): “ationally faster neural architecture which performs reasonably well on SQuAD task. 2 Chapter 2 Literature Review Most of the papers that approach to solve SQuAD problem have utilized RNN based models [3, 5, 7, 9, 13, 14, 16, 17, 20, 22{24, 26, 29{31, 34{39, 41, 43] and they follow a similar chain of”
Multi-range Reasoning for Machine Comprehension (2018, background): “Recent work also investigates the notion of multi-hop reasoning [7, 25, 37], reinforcement learning [25, 32, 11] and self-matching / self-attention [17, 33].”
Densely Connected Attention Propagation for Reading Comprehension (2018, background): “…[Xiong et al., 2016, 2017], R-NET [Wang et al., 2017c], DrQA [Chen et al., 2017], AoA Reader [Cui et al., 2016], Reinforced Mnemonic Reader [Hu et al., 2017], ReasoNet [Shen et al., 2017], AMANDA [Kundu and Ng, 2018], R 3 Reinforced Reader Ranker [Wang et al., 2017a] and QANet [Yu et al.,…”
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (2018, background): “For QA over text only, a major focus has been on the task of reading comprehension (Seo et al., 2017; Gong and Bowman, 2017; Hu et al., 2017; Shen et al., 2017; Yu et al., 2018) since the introduction of SQuAD (Rajpurkar et al., 2016).”
Self-Training for Jointly Learning to Ask and Answer Questions (2018, methodology): “After the release of large benchmarks such as SQUAD , MS MARCO and WikiQA , there has been a surge in interest on using neural network or deep-learning models for QA (Yin et al., 2015; Seo et al., 2016; Shen et al., 2016; Chen et al., 2017; Liu et al., 2017; Hu et al., 2017).”