[PDF] Top 20 Robust Machine Comprehension Models via Adversarial Training
Has 10000 "Robust Machine Comprehension Models via Adversarial Training" found on our website. Below are the top 20 most common "Robust Machine Comprehension Models via Adversarial Training".
Robust Machine Comprehension Models via Adversarial Training
... during training, the model is rarely punished for answer- ing questions based on syntactic similarity, and learns it as a reliable approach to ...The models’ failures on AddSent demonstrates their ignorance ... See full document
7
Loss Sensitive Discriminative Training of Machine Transliteration Models
... during training, we find that making small incremental updates makes our algo- rithm more ...and training on the train+dev set improves 5-best per- formance of our model from ... See full document
5
CALOR QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations
... Beyond knowledge-based pattern-based ap- proaches, recent work consider question genera- tion as a supervised machine learning task where questions or question patterns are generated by an end-to-end neural ... See full document
8
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior
... current models solely takes the phrase-level information into account when predicting the probability p(a|q, p), but fails to exploit the sentence-level matching between the answer-contained sentence and the ... See full document
5
Proposed Improvements For Automated Chemical Safety Evaluations Using In-Silico Techniques
... in machine-learning (ML) due to advances in hardware and the dividends of networked research-communities coming into fruition in the form of enriched datasets, researchers can now correctly leverage in-silico ... See full document
8
What’s in a Domain? Learning Domain Robust Text Representations using Adversarial Training
... learning robust models which generalise well to both similar (in domain) and dissimilar (out of domain) instances to those seen in ...with adversarial training for ... See full document
6
LexicalAT: Lexical Based Adversarial Reinforcement Training for Robust Sentiment Classification
... words under two different settings. Surprisingly, the neighbor words largely contribute to the per- formance. Without neighbor words, the average accuracies are dropped from 66.17 to 65.38 for RNN, and from 65.63 to ... See full document
10
Linear Mixture Models for Robust Machine Translation
... We present an empirical investigation of all the variations outlined above using a strong system trained on large and diverse training corpora, for two language pairs and two distinct test domains. Our results ... See full document
11
Domain-Adversarial Training of Neural Networks
... new machine learning task is often an obstacle for applying machine learning ...of machine-learning tasks and appli- ...obtain training sets that are big enough for training large-scale ... See full document
35
Generating Fluent Adversarial Examples for Natural Languages
... Generally, adversarial attacks aim to mislead the neural models by feeding adversarial examples with perturbations, while adversarial training aims to improve the models by ... See full document
6
Combating Adversarial Misspellings with Robust Word Recognition
... combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream ...recognition models build upon the RNN semi- character architecture, introducing several new ... See full document
10
On the Robustness of Self Attentive Models
... struct adversarial examples by solving a discrete optimization ...generate adversarial attacks against black-box models for applications includ- ing image classification, textual entailment, and ... See full document
10
Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension
... and models for English language, which has enormous amount of labeled and unlabeled re- ...for training. Another difficulty arises, when the same models are being applied to more agglutinative ... See full document
5
Adversarial Training for Relation Extraction
... network models tend to be overconfident about the noise in input ...signals. Adversarial examples (Szegedy et ...model. Adversarial training (Goodfel- low et ...deep models by ... See full document
6
Adversarial Domain Adaptation for Machine Reading Comprehension
... 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 ...an Adversarial Domain Adaptation framework ... See full document
11
Robust Neural Machine Translation with Doubly Adversarial Inputs
... a robust NMT model that is able to overcome small perturba- tions in the input ...Overcoming adversarial examples in NMT is a challenging problem as the words in the input are represented as discrete ... See full document
10
Compact and Robust Models for Japanese English Character level Machine Translation
... our models are similar to the subword-level model of Morishita et ...our training is much ...for training in the experiment with the best result in a single ...and training time are shown in ... See full document
9
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
... DARPA BOLT is a major research project with the goal of improving translation of informal, dialec- tical Arabic and Chinese into English. The BOLT domain presented here is “web forum,” which was crawled from various ... See full document
11
Robust Multilingual Part of Speech Tagging via Adversarial Training
... Character-level BiLSTM. Prior work has shown that incorporating character-level represen- tations of words can boost POS tagging accuracy by capturing morphological information present in each language. Major neural ... See full document
11
Training Gaussian Mixture Models at Scale via Coresets
... clustering via exhaustive search, by simply considering all possible partitions of the coreset, and picking the best ...mixture models, this exhaustive search algorithm is not feasible, since points are not ... See full document
25
Related subjects