photoML
Maria Leonor Pacheco
CS Department - Purdue University
Advisor: Prof. Dan Goldwasser
pachecog [at] purdue [dot] edu
mlpacheco
google scholar
CV

I am a PhD. candidate in Computer Science at Purdue University, where I work with Dr. Dan Goldwasser as part of the Purdue Natural Language Processing Group. My current research focuses broadly on neural-symbolic methods to model natural language discourse. My domains of interest include argumentative texts, online conversations and narratives.

I am also actively involved in interdisciplinary collaborations with the Network and Dependable Systems and Security Lab at Northeastern University. This work focuses on leveraging NLP for network security.


News


Publications

Refereed Journals and Conferences (* Indicates co-first authorship)
Identifying Morality Frames in Political Tweets Using Relational Learning
S Roy, ML Pacheco, D Goldwasser
In Empirical Methods in Natural Language Processing, EMNLP 2021.
Modeling Content and Context with Deep Relational Learning
ML Pacheco, D Goldwasser
In Transactions of the Association for Computational Linguistics, TACL 2021.
Presented at NAACL 2021
Modeling Human Mental States with an Entity-based Narrative Graph
IT Lee, ML Pacheco, D Goldwasser
In North American Chapter of the Association for Computational Linguistics, NAACL 2021.
Randomized Deep Structured Prediction for Discourse-level Processing
M Widmoser*, ML Pacheco*, J Honorio, D Goldwasser
In European Chapter of the Association for Computational Linguistics, EACL 2021.
Also presented at the EMNLP 2020 Workshop on Structured Prediction for NLP
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations
IT Lee, ML Pacheco, D Goldwasser
In Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2020.
Identifying Collaborative Conversations using Latent Discourse Behaviors
A Jain, ML Pacheco, S Lancette, M Goindani, D Goldwasser
In ACL Special Interest Group on Discourse and Dialogue, SIGDIAL 2020.
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
S Jero, ML Pacheco, D Goldwasser, C Nita-Rotaru
In Innovative Applications of Artificial Intelligence, IAAI 2019.
Adapting Event Embeddings for Implicit Discourse Relations
ML Pacheco, IT Lee, X Zhang, AK Zehady, P Daga, D Jin, A Parolia, D Goldwasser
In Computational Natural Language Learning, CoNLL Shared Task 2016.
Refereed Workshops (* Indicates co-first authorship)
Neural-Symbolic Modeling for Natural Language Discourse
ML Pacheco, D Goldwasser
ICML 2020 Workshop on Bridge Between Perception and Reasoning
[Paper] [Poster]
Leveraging Representation and Inference through Deep Relational Learning
ML Pacheco, I Dalal, D Goldwasser
NeurIPS 2018 Workshop on Relational Representation Learning
[Paper] [Poster]
Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
IT Lee, M Goindani, C Li, D Jin, K Johnson, X Zhang, ML Pacheco, D Goldwasser
In International Workshop on Semantic Evaluation, SemEval 2017
[Paper] [Poster]
Introducing DRaiL: A Step Towards Declarative Deep Relational Learning
X Zhang*, ML Pacheco*, C Li, D Goldwasser
EMNLP 2016 Workshop on Structured Prediction for NLP
[Paper] [Poster]

Teaching

Academic and Instutional Service