Javid Ebrahimi


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How Can Self-Attention Networks Recognize Dyck-n Languages?
Javid Ebrahimi | Dhruv Gelda | Wei Zhang
Findings of the Association for Computational Linguistics: EMNLP 2020

We focus on the recognition of Dyck-n (Dn) languages with self-attention (SA) networks, which has been deemed to be a difficult task for these networks. We compare the performance of two variants of SA, one with a starting symbol (SA+) and one without (SA-). Our results show that SA+ is able to generalize to longer sequences and deeper dependencies. For D2, we find that SA- completely breaks down on long sequences whereas the accuracy of SA+ is 58.82%. We find attention maps learned by SA+ to be amenable to interpretation and compatible with a stack-based language recognizer. Surprisingly, the performance of SA networks is at par with LSTMs, which provides evidence on the ability of SA to learn hierarchies without recursion.


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On Adversarial Examples for Character-Level Neural Machine Translation
Javid Ebrahimi | Daniel Lowd | Dejing Dou
Proceedings of the 27th International Conference on Computational Linguistics

Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of NLP models have been done through black-box adversarial examples. We investigate adversarial examples for character-level neural machine translation (NMT), and contrast black-box adversaries with a novel white-box adversary, which employs differentiable string-edit operations to rank adversarial changes. We propose two novel types of attacks which aim to remove or change a word in a translation, rather than simply break the NMT. We demonstrate that white-box adversarial examples are significantly stronger than their black-box counterparts in different attack scenarios, which show more serious vulnerabilities than previously known. In addition, after performing adversarial training, which takes only 3 times longer than regular training, we can improve the model’s robustness significantly.

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HotFlip: White-Box Adversarial Examples for Text Classification
Javid Ebrahimi | Anyi Rao | Daniel Lowd | Dejing Dou
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.


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Weakly Supervised Tweet Stance Classification by Relational Bootstrapping
Javid Ebrahimi | Dejing Dou | Daniel Lowd
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
Javid Ebrahimi | Dejing Dou | Daniel Lowd
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting.


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Chain Based RNN for Relation Classification
Javid Ebrahimi | Dejing Dou
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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A New Parametric Estimation Method for Graph-based Clustering
Javid Ebrahimi | Mohammad Saniee Abadeh
Workshop Proceedings of TextGraphs-7: Graph-based Methods for Natural Language Processing