Papers by Tim O’Gorman
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions (2022.naacl-main)
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Neha Kennard, Tim O’Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, Andrew McCallum
| Challenge: | Prior work on labeling arguments extracted from peer review text has focused qualified labor force on labelling arguments extracted by the text. |
| Approach: | They synthesize label sets from prior work and extend them to include fine-grained annotations of review and rebuttal sentences. |
| Outcome: | The proposed dataset synthesizes label sets from prior work and extends them to include fine-grained annotation of review and rebuttal sentences. |
MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text (2021.emnlp-main)
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Tim O’Gorman, Zach Jensen, Sheshera Mysore, Kevin Huang, Rubayyat Mahbub, Elsa Olivetti, Andrew McCallum
| Challenge: | Material science synthesis procedures require high-quality annotations, which are limited by the size and quality of the annotations. |
| Approach: | They propose a corpus of entity mention annotations over 595 Material Science synthesis procedures. |
| Outcome: | The proposed approach greatly expands the training data available for the Named Entity Recognition task. |
AMR Beyond the Sentence: the Multi-sentence AMR corpus (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is limited to capturing the semantics of individual sentences. |
| Approach: | They propose a corpus that annotates coreference and similar phenomena on top of existing AMRs. |
| Outcome: | The proposed corpus is compared with existing corpora on sentence-level semantics . it shows that it can be used for information extraction and question answering . |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
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Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures (2024.acl-long)
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Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Li
| Challenge: | Existing commonsense evaluations are often posed as multiple-choice questions, allowing models to exploit systematic biases. |
| Approach: | They propose a generative task that evaluates common sense via multiple open-ended generations and a method that strongly correlates with human judgments. |
| Outcome: | The proposed method outperforms strong language model baselines on a dataset of human and machine common sense. |
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text (2020.findings-emnlp)
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| Challenge: | Existing approaches to parse scientific text using grammatically similar labeled sentences are limited and expensive to create. |
| Approach: | They propose a method where semantic labels from structurally similar sentences are copied to test sentences. |
| Outcome: | The proposed approach outperforms baseline and prior methods by 0.75 to 3 F1 absolute in the wet lab protocol corpus and 1 F1 absolut in the materials science procedural text corpus. |
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning (2020.emnlp-main)
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| Challenge: | Existing question answering datasets for common sense reasoning are lacking for prototypical situations. |
| Approach: | They propose a question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. |
| Outcome: | The proposed model outperforms existing models on all evaluation metrics with a meaningful gap. |
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
| Approach: | They propose to expand the AMR project's lexicon of predicate senses to include entries for a growing set of constructions. |
| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
The New Propbank: Aligning Propbank with AMR through POS Unification (L18-1)
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| Challenge: | Existing Propbank corpus converts sense labels to a format which is more compatible with AMR and more robust to sparsity. |
| Approach: | They propose a corpus which converts existing Propbank sense labels to a new unified format which is more compatible with AMR and more robust to sparsity. |
| Outcome: | The proposed format is more compatible with AMR and robust to sparsity. |
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders (2020.emnlp-main)
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| Challenge: | Syntactic parse trees are valuable intermediate features for many NLP pipelines. |
| Approach: | They propose an improved version of DIORA that encodes a single tree rather than a softly-weighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. |
| Outcome: | The proposed model improves state-of-the-art in constituency parsing on the English WSJ Penn Treebank by 2.2-6% F1, depending on the data used for fine-tuning. |
Improved Latent Tree Induction with Distant Supervision via Span Constraints (2021.emnlp-main)
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Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum
| Challenge: | Distant supervision is not a practical way to perform unsupervised syntactic parsing. |
| Approach: | They propose a technique that uses distant supervision to improve unsupervised constituency parsing by using phrase bracketing. |
| Outcome: | The proposed method improves constituency parsing on English WSJ Penn Treebank by more than 5 F1 compared with full parse tree annotations. |