In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: Neural models achieved considerable improvement for many natural language processing tasks, but they offer little transparency, and interpretability comes at a cost. In some domains, automated predictions without justifications have limited applicability. Recently, progress has been made regarding single-aspect sentiment analysis for reviews, where the ambiguity of a justification is minimal. In this context, a justification, or mask, consists of (long) word sequences from the input text, which suffice to make the prediction. Existing models cannot handle more than one aspect in one training and induce binary masks that might be ambiguous. In our work, we propose a neural model for predicting multi-aspect sentiments for reviews and generates a probabilistic multi-dimensional mask (one per aspect) simultaneously, in an unsupervised and multi-task learning manner. Our evaluation shows that on three datasets, in the beer and hotel domain, our model outperforms strong baselines and generates masks that are: strong feature predictors, meaningful, and interpretable.

A: Neural model predicting multi-aspect sentiments and generating a probabilistic multi-dimensional mask simultaneously. Model outperforms strong baselines and generates masks that are: strong feature predictors, meaningful, and interpretable.
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Q: Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.

A: We propose a framework to generate “natural” adversaries against black-box classifiers for both visual and textual domains, by doing the search for adversaries in the latent semantic space.
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Q: Recent pretrained transformer-based language models have set state-of-the-art performances on various NLP datasets. However, despite their great progress, they suffer from various structural and syntactic biases. In this work, we investigate the lexical overlap bias, e.g., the model classifies two sentences that have a high lexical overlap as entailing regardless of their underlying meaning. To improve the robustness, we enrich input sentences of the training data with their automatically detected predicate-argument structures. This enhanced representation allows the transformer-based models to learn different attention patterns by focusing on and recognizing the major semantically and syntactically important parts of the sentences.   We evaluate our solution for the tasks of natural language inference and grounded commonsense inference using the BERT, RoBERTa, and XLNET models. We evaluate the models' understanding of syntactic variations, antonym relations, and named entities in the presence of lexical overlap. Our results show that the incorporation of predicate-argument structures during fine-tuning considerably improves the robustness, e.g.,  about 20pp on discriminating different named entities, while it incurs no additional cost at the test time and does not require changing the model or the training procedure.

A:
Enhancing the robustness of pretrained transformer models against the lexical overlap bias by extending the input sentences of the training data with their corresponding predicate-argument structures 
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