Papers by Clayton Morrison
Validity Assessment of Legal Will Statements as Natural Language Inference (2022.findings-emnlp)
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| Challenge: | This study introduces a dataset that focuses on the validity of statements in legal wills. |
| Approach: | They propose a dataset that focuses on the validity of statements in legal wills. |
| Outcome: | The proposed model achieves 80% macro F1 and accuracy, but group accuracy is in mid 80s at best, suggesting that the models’ understanding of the task remains superficial. |
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions (2020.lrec-1)
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Maria Alexeeva, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Jennifer Kadowaki, Adarsh Pyarelal, Clayton Morrison
| Challenge: | Existing approaches to extract mathematical concepts and their descriptions are useful for a variety of tasks, including math information retrieval and accessibility efforts to make scientific documents available to the visually impaired. |
| Approach: | They propose a rule-based approach which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. |
| Outcome: | The proposed approach extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. |
Information Extraction from Legal Wills: How Well Does GPT-4 Do? (2023.findings-emnlp)
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| Challenge: | Using information extraction from legal wills is an important application of artificial intelligence (AI) |
| Approach: | They propose a manually annotated dataset for Information Extraction (IE) from legal wills . they also use it to evaluate the performance of large language models (LLMs) |
| Outcome: | The proposed dataset can be used to evaluate large language models on IE from legal wills . it shows that the model performs reasonably well, but inconsistent outputs and overgeneralization are observed . |
Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models (N19-4)
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Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
| Challenge: | a paper proposes a method for building probabilistic models of complex phenomena such as food insecurity . currently, these models are hand-built for each new situation and require months to construct . |
| Approach: | They propose an approach that builds executable probabilistic models from raw, free text. |
| Outcome: | The proposed approach builds executable probabilistic models from raw, free text. |
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference (L18-1)
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| Challenge: | Existing methods of automated inference do not provide enough gold explanations to train models . standardized science exams are a challenge task for question answering . |
| Approach: | They propose to manually construct a corpus of explanations for standardized science exams . they also provide an explanation-centered tablestore that contains the knowledge to construct these explanations . |
| Outcome: | The proposed model provides detailed explanations for standardized science exams . the authors show that the proposed model can be trained on the basis of gold explanations . |
When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context (2024.findings-emnlp)
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Enrique Noriega-Atala, Robert Vacareanu, Salena Ashton, Adarsh Pyarelal, Clayton Morrison, Mihai Surdeanu
| Challenge: | a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurately predict the relevant scenario information. |
| Approach: | They propose a neural architecture finetuned for the task of scenario context generation . they use a curated dataset of time and location annotations to train an encoder-decoder architecture . |
| Outcome: | The proposed model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurately predict the relevant scenario information of a particular entity or event. |