Alvin Po-Chun Chen, Rohan Das, Dananjay Srinivas, Alexandra Barry, Maksim Seniw, Maria Leonor Pacheco. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Rohan Das, Advait Deshmukh, Alexandria Leto, Zohar Naaman, I-Ta Lee, Maria Leonor Pacheco. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Shira Wein, Anna Serbina, Jiyuan Ji, Nathan Wolf, Jason DeGraaff, Prajakta Kini, Maria Leonor Pacheco. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Alexandria Leto, Elliot Pickens, Coen Needell, David Rothschild, Maria Leonor Pacheco. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular.However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs.Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society.We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework.After embedding this information, we formulate inference operators which augment the graph edges by revealing unobserved interactions between its elements, such as similarity between documents' contents and users' engagement patterns.Our experiments over two challenging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.

Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Automated attack discovery techniques, such as attacker synthesis or model-based fuzzing, provide powerful ways to ensure network protocols operate correctly and securely. Such techniques, in general, require a formal representation of the protocol, often in the form of a finite state machine (FSM). Unfortunately, many protocols are only described in English prose, and implementing even a simple network protocol as an FSM is time-consuming and prone to subtle logical errors. Automatically extracting protocol FSMs from documentation can significantly contribute to increased use of these techniques and result in more robust and secure protocol implementations.In this work we focus on attacker synthesis as a representative technique for protocol security, and on RFCs as a representative format for protocol prose description. Unlike other works that rely on rule-based approaches or use off-the-shelf NLP tools directly, we suggest a data-driven approach for extracting FSMs from RFC documents. Specifically, we use a hybrid approach consisting of three key steps: (1) large-scale word-representation learning for technical language, (2) focused zero-shot learning for mapping protocol text to a protocol-independent information language, and (3) rule-based mapping from protocol-independent information to a specific protocol FSM. We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP. We demonstrate how automated extraction of an FSM from an RFC can be applied to the synthesis of attacks, with TCP and DCCP as case-studies. Our approach shows that it is possible to automate attacker synthesis against protocols by using textual specifications such as RFCs.

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, capturing the dependencies between words in a single sentence, or pairs of sentences.However, certain tasks, such as argumentation mining, require accounting for longer texts and complicated structural dependencies between them.Deep structured prediction is a general framework to combine the complementary strengths of expressive neural encoders and structured inference for highly structured domains.Nevertheless, when the need arises to go beyond sentences, most work relies on combining the output scores of independently trained classifiers.One of the main reasons for this is that constrained inference comes at a high computational cost.In this paper, we explore the use of randomized inference to alleviate this concern and show that we can efficiently leverage deep structured prediction and expressive neural encoders for a set of tasks involving complicated argumentative structures.

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 foundations, each associated with a positive and negative polarity.However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities.In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and highquality annotated dataset of tweets written by US politicians.Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly.We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.

Understanding narrative text requires capturing characters' motivations, goals, and mental states.This paper proposes an Entity-based Narrative Graph (ENG) to model the internalstates of characters in a story.We explicitly model entities, their interactions and the context in which they appear, and learn rich representations for them.We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space.We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

In this work, we study collaborative online conversations.Such conversations are rich in content, constructive and motivated by a shared goal.Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions.To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks.These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread.We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.

Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.