María Leonor Pacheco

Also published as: Maria Leonor Pacheco


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Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations
I-Ta Lee | Maria Leonor Pacheco | Dan Goldwasser
Findings of the Association for Computational Linguistics: EMNLP 2020

Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types. This paper suggests to represent this type of information as a narrative graph and learn contextualized event representations over it using a relational graph neural network model. We train our model to capture event relations, derived from the Penn Discourse Tree Bank, on a huge corpus, and show that our multi-relational contextualized event representation can improve performance when learning script knowledge without direct supervision and provide a better representation for the implicit discourse sense classification task.

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Identifying Collaborative Conversations using Latent Discourse Behaviors
Ayush Jain | Maria Leonor Pacheco | Steven Lancette | Mahak Goindani | Dan Goldwasser
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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.


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PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
I-Ta Lee | Mahak Goindani | Chang Li | Di Jin | Kristen Marie Johnson | Xiao Zhang | Maria Leonor Pacheco | Dan Goldwasser
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.


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Adapting Event Embedding for Implicit Discourse Relation Recognition
Maria Leonor Pacheco | I-Ta Lee | Xiao Zhang | Abdullah Khan Zehady | Pranjal Daga | Di Jin | Ayush Parolia | Dan Goldwasser
Proceedings of the CoNLL-16 shared task

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Introducing DRAIL – a Step Towards Declarative Deep Relational Learning
Xiao Zhang | Maria Leonor Pacheco | Chang Li | Dan Goldwasser
Proceedings of the Workshop on Structured Prediction for NLP