James Fiacco


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Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation
James Fiacco | Shiyan Jiang | David Adamson | Carolyn Rosé
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

Providing effective automatic essay feedback is necessary for offering writing instruction at a massive scale. In particular, feedback for promoting coherent flow of ideas in essays is critical. In this paper we propose a state-of-the-art method for automated analysis of structure and flow of writing, referred to as Rhetorical Structure Theory (RST) parsing. In so doing, we lay a foundation for a generalizable approach to automated writing feedback related to structure and flow. We address challenges in automated rhetorical analysis when applied to student writing and evaluate our novel RST parser model on both a recent student writing dataset and a standard benchmark RST parsing dataset.


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Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance
Sopan Khosla | James Fiacco | Carolyn Rosé
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.


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Deep Neural Model Inspection and Comparison via Functional Neuron Pathways
James Fiacco | Samridhi Choudhary | Carolyn Rose
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a general method for the interpretation and comparison of neural models. The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways. The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task. As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.