Adam Dalton


2020

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Learning to Plan and Realize Separately for Open-Ended Dialogue Systems
Sashank Santhanam | Zhuo Cheng | Brodie Mather | Bonnie Dorr | Archna Bhatia | Bryanna Hebenstreit | Alan Zemel | Adam Dalton | Tomek Strzalkowski | Samira Shaikh
Findings of the Association for Computational Linguistics: EMNLP 2020

Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems. We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes. To investigate, we decouple generation into two separate phases: planning and realization. In the planning phase, we train two planners to generate plans for response utterances. The realization phase uses response plans to produce an appropriate response. Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.

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Active Defense Against Social Engineering: The Case for Human Language Technology
Adam Dalton | Ehsan Aghaei | Ehab Al-Shaer | Archna Bhatia | Esteban Castillo | Zhuo Cheng | Sreekar Dhaduvai | Qi Duan | Bryanna Hebenstreit | Md Mazharul Islam | Younes Karimi | Amir Masoumzadeh | Brodie Mather | Sashank Santhanam | Samira Shaikh | Alan Zemel | Tomek Strzalkowski | Bonnie J. Dorr
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We describe a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. The system processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the system is that it uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker’s time and resources.

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Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation
Archna Bhatia | Adam Dalton | Brodie Mather | Sashank Santhanam | Samira Shaikh | Alan Zemel | Tomek Strzalkowski | Bonnie J. Dorr
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We present a paradigm for extensible lexicon development based on Lexical Conceptual Structure to support social engineering detection and response generation. We leverage the central notions of ask (elicitation of behaviors such as providing access to money) and framing (risk/reward implied by the ask). We demonstrate improvements in ask/framing detection through refinements to our lexical organization and show that response generation qualitatively improves as ask/framing detection performance improves. The paradigm presents a systematic and efficient approach to resource adaptation for improved task-specific performance.

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Email Threat Detection Using Distinct Neural Network Approaches
Esteban Castillo | Sreekar Dhaduvai | Peng Liu | Kartik-Singh Thakur | Adam Dalton | Tomek Strzalkowski
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.

2015

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A New Dataset and Evaluation for Belief/Factuality
Vinodkumar Prabhakaran | Tomas By | Julia Hirschberg | Owen Rambow | Samira Shaikh | Tomek Strzalkowski | Jennifer Tracey | Michael Arrigo | Rupayan Basu | Micah Clark | Adam Dalton | Mona Diab | Louise Guthrie | Anna Prokofieva | Stephanie Strassel | Gregory Werner | Yorick Wilks | Janyce Wiebe
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2013

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Automatic Metaphor Detection using Large-Scale Lexical Resources and Conventional Metaphor Extraction
Yorick Wilks | Adam Dalton | James Allen | Lucian Galescu
Proceedings of the First Workshop on Metaphor in NLP