[PDF] Top 20 Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension

... More large-scale dataset was introduced by Zhang et al. (2018). Authors designed a mul- tistage procedure to generate passage-question- answer triplets from CNN/Daily Mail dataset and Internet Archive which included ... See full document

... MRC models have embraced this kind of multi- step strategy, where predictions are generated after making multiple passes through the same text and integrating intermediate information in the pro- ...first ... See full document

... External Knowledge In the original paper of ReCoRD (Zhang et ...existing models that use external knowledge to improve MRC ...lational knowledge paths from ...trieves knowledge triples ... See full document

... Textual entailment is required to solve 60% of the questions, such as the question “What do Matt, Steve, and Andrew record themselves do- ing weeks after their experience in the woods?” with the relevant sentence “Weeks ... See full document

... convolutional neural network (CNN) is explored to model all types of ...system models the document bottom-up, through accumulating the most relevant information from word level to snippet ... See full document

... the neural networks of MRC models with the general knowl- edge of human ...general knowledge to an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the general ... See full document

... Language Models alone can model commonsense reasoning better than the other models incorporating com- monsense knowledge base resources like Concept- Net, NELL, etc integrated with deep ... See full document

... possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a ...for machine reading ... See full document

... and knowledge bases, we ex- plore learning commonsense knowledge from a large amount of raw text via unsupervised ...two neural network models based on the Deep Structured ... See full document

... attention-based neural network approaches (Bahdanau et al., 2014), the machine is able to learn the relationships between document, query and ...the neural network based ... See full document

... multiple-choice machine reading comprehension datasets (Table 2, Table 3, and Table 4 in Sec- tion ...features based on pre-trained word/paraphrase embeddings and external knowledge ... See full document

... by machine reading comprehension (MRC) problems in ...while reading the ...graph neural network to perform numerical reasoning on the ... See full document

... Many neural networks have been proposed to solve answer span QA ...the models adopted pointer networks as a prediction layer and then focused on improving other ...select machine translation model ... See full document

... sequence-to-sequence models covering a variety of tasks such as machine translation, summarisa- tion, speech processing and question answering as NMT ...NMT models had to be lim- ... See full document

... of commonsense knowledge our models struggle to capture, we report the top 100 most confident predictions that receive an average score below 3 in the supplementary ... See full document

... child-based reading constructs and measures when investigating the reading skills of adults struggling with ...child-based reading constructs and assessments with adults reading ... See full document

... Parameters are updated by Mini-batch Gra- dient Descent and the learning rate is con- trolled by the AdaDelta (Zeiler, 2012) algorith- m with decay constant ρ = 0.95 and denomi- nator constant ϵ = 1e − 6. The batch size ... See full document

... hybrid neural network (HNN) model that combines the strengths of both neural language models and a semantic similarity ...ponent models, a masked language model and a deep semantic ... See full document

... Automatic metrics that contrast system outputs against ref- erence translations, such as BLEU (Papineni et al., 2002), are widely explored to compare MT systems and measure the progress of a given MT system over time. ... See full document

... transferring knowledge learned from one domain to another. Based on this, an answer decoder is then used to decode domain-invariant representa- tion into an answer ... See full document