59 citations · 169 across the 28 of their papers we have counts for
29 papers
RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines
Anqi Chen, Dan Goldwasser, Cristina Nita-Rotaru
Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-generated mappings, used for netwo…
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
Tunazzina Islam, Ruqi Zhang, Dan Goldwasser
Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective acti…
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Maria Leonor Pacheco, Tunazzina Islam, Lyle Ungar +2
Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniqu…
Towards Few-Shot Identification of Morality Frames using In-Context Learning
Shamik Roy, Nishanth Sridhar Nakshatri, Dan Goldwasser
Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot ide…
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair
Nan Jiang, Thibaud Lutellier, Yiling Lou +3
Automated Program Repair (APR) improves software reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build mod…
Towards Explaining Subjective Ground of Individuals on Social Media
Younghun Lee, Dan Goldwasser
Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior fr…
Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision
Tunazzina Islam, Dan Goldwasser
In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a su…
Weakly Supervised Learning for Analyzing Political Campaigns on Facebook
Tunazzina Islam, Shamik Roy, Dan Goldwasser
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, mak…
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan +4
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifyi…
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents
Maria Leonor Pacheco, Max von Hippel, Ben Weintraub +2
Automated attack discovery techniques, such as attacker synthesis or model-based fuzzing, provide powerful ways to ensure network protocols operate correctly and securely. Such tec…
Identifying Morality Frames in Political Tweets using Relational Learning
Shamik Roy, Maria Leonor Pacheco, Dan Goldwasser
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral…
Twitter User Representation Using Weakly Supervised Graph Embedding
Tunazzina Islam, Dan Goldwasser
Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, this ra…
Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model
Tunazzina Islam, Dan Goldwasser
Multiview representation learning of data can help construct coherent and contextualized users' representations on social media. This paper suggests a joint embedding model, incorp…
Modeling Human Mental States with an Entity-based Narrative Graph
I-Ta Lee, Maria Leonor Pacheco, Dan Goldwasser
Understanding narrative text requires capturing characters' motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal-s…
Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information
Tunazzina Islam, Dan Goldwasser
Leveraging social media data to understand people's lifestyle choices is an exciting domain to explore but requires a multiview formulation of the data. In this paper, we propose a…
Randomized Deep Structured Prediction for Discourse-Level Processing
Manuel Widmoser, Maria Leonor Pacheco, Jean Honorio +1
Expressive text encoders such as RNNs and Transformer Networks have been at the center of NLP models in recent work. Most of the effort has focused on sentence-level tasks, capturi…
Using Natural Language Relations between Answer Choices for Machine Comprehension
Rajkumar Pujari, Dan Goldwasser
When evaluating an answer choice for Reading Comprehension task, other answer choices available for the question and the answers of related questions about the same paragraph often…
Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information
Tunazzina Islam, Dan Goldwasser
Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state…
Understanding Politics via Contextualized Discourse Processing
Rajkumar Pujari, Dan Goldwasser
Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite o…
Semi-supervised Autoencoding Projective Dependency Parsing
Xiao Zhang, Dan Goldwasser
We describe two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing. The first model is a Locally Autoencoding Parser (LAP) encoding the in…
Cross-Lingual Document Retrieval with Smooth Learning
Jiapeng Liu, Xiao Zhang, Dan Goldwasser +1
Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language. In this paper, we study the instability of neura…
"where is this relationship going?": Understanding Relationship Trajectories in Narrative Text
Keen You, Dan Goldwasser
We examine a new commonsense reasoning task: given a narrative describing a social interaction that centers on two protagonists, systems make inferences about the underlying relati…
Modeling Content and Context with Deep Relational Learning
Maria Leonor Pacheco, Dan Goldwasser
Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have e…
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media
Shamik Roy, Dan Goldwasser
In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad polic…