[PDF] Top 20 A Neural Model of Adaptation in Reading

... the model generated more human-like ...the model adapted both its lexical and its syntac- tic predictions, in line with findings from human ...other neural-network based models in psychology (Chang ... See full document

... main adaptation for Machine Reading Com- prehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target ...Domain Adaptation ... See full document

... new neural models from scratch for each of the pipeline ...AE-SCL model of Ziser and Reichart, rather than their subsequent models that take embeddings as ...AE-SCL model can obtain state-of-the-art ... See full document

... Typically, we will process day by day: after one day of work, all the human translations (cre- ated from scratch or by post-editing the hypothe- ses from the SMT system) are injected into the system and we hope that SMT ... See full document

... domain adaptation techniques, data selec- tion and corpus weighting, to neural machine translation ...for neural machine trans- lation domain ...translation model simultaneously; in-domain ... See full document

... RNNLM adaptation is used as part of the lightly supervised decoding process, in order to get an output that most closely matches the ground ...language model trained on LM1&LM 2 ...the model is ... See full document

... attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built us- ing eye-tracking data as dependent data and other features in the context as in- ... See full document

... out-domain model, and only needs to process the in-domain cor- ...during adaptation, this is suitable for scenarios where adaptation must be performed quickly or where the original out- domain corpus ... See full document

... pre-existing model is very ...trained model on newly acquired data [39]. While training a CNN model as FG-UNET for every map to be produced is too costly, fine-tuning the model has a cost ... See full document

... SLT adaptation of Transformer (the state-of-the-art architecture in MT), which exploits the integration of ASR solutions to cope with long input sequences featur- ing low information ...our adaptation is ... See full document

... whole, reading is a recent innovation in evolutionary terms, and peo- ple learning to read do not have access to compe- tent readers’ eye-movement patterns as training ... See full document

... The model obeys the original Hebbian learning rule. The reading process is simulated at that model in analogues manner to the previous simulation for Pavlovian conditioning ...the model are ... See full document

... Machine reading systems (Poon et al., 2010; Richardson et al., 2013) can be tested on their ability to answer queries about contents of doc- uments that they read, thus a central problem is how the information of ... See full document

... domain adaptation where the K classification tasks are different “domains” and we aim to learn a global parameter Θ shared across the ...3.3 Neural Networks for Sequence ... See full document

... linear model describing individual subject (circles) and model (each diamond represents analyses based on the same sound sequence experienced by the subject connected by a line) perceptual bias in terms of ... See full document

... Although neural conversation models are ef- fective in learning how to produce fluent re- sponses, their primary challenge lies in know- ing what to say to make the conversation con- tentful and ...contentful ... See full document

... In neural network training, words are represented as distributed representations, so-called “word vec- tors”, which can be pre-trained or trained with a specific task in ...vector model is also learned with ... See full document

... language model on the selected data and decode the test audio ...a neural network language ...and neural net- work language model improves it up to ... See full document

... (Artificial) Neural Networks [11] are computational models inspired by biological nervous systems and generally presented as systems of interconnected ‘neurons’ which can compute values from ...The ... See full document

... Table 3 shows two examples of the translated sentences which include unknown words, and for each example, its reference translation (R.) and its translation result (T.) are shown. The replaced unknown words are ... See full document