@inproceedings{ahmed-etal-2024-make,title={Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing},author={Ahmed, Shafiuddin Rehan and Wang, Zhiyong and Baker, George and Stowe, Kevin and Martin, James H.},eprint={2407.11988},archiveprefix={arXiv},primaryclass={cs.CL},url={https://arxiv.org/abs/2407.11988},month=jun,year={2024},}
Linear Cross-document Event Coreference Resolution with X-AMR
Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, Michael Reagan, and
3 more authors
In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024
Event Coreference Resolution (ECR) as a pairwise mention classification task is expensive both for automated systems and manual annotations. The task’s quadratic difficulty is exacerbated when using Large Language Models (LLMs), making prompt engineering for ECR prohibitively costly. In this work, we propose a graphical representation of events, X-AMR, anchored around individual mentions using a cross-document version of Abstract Meaning Representation. We then linearize the ECR with a novel multi-hop coreference algorithm over the event graphs. The event graphs simplify ECR, making it a) LLM cost-effective, b) compositional and interpretable, and c) easily annotated. For a fair assessment, we first enrich an existing ECR benchmark dataset with these event graphs using an annotator-friendly tool we introduce. Then, we employ GPT-4, the newest LLM by OpenAI, for these annotations. Finally, using the ECR algorithm, we assess GPT-4 against humans and analyze its limitations. Through this research, we aim to advance the state-of-the-art for efficient ECR and shed light on the potential shortcomings of current LLMs at this task. Code and annotations: \urlhttps://github.com/ahmeshaf/gpt_coref
@inproceedings{ahmed-etal-2024-linear-cross,title={Linear Cross-document Event Coreference Resolution with {X}-{AMR}},author={Ahmed, Shafiuddin Rehan and Baker, George Arthur and Judge, Evi and Reagan, Michael and Wright-Bettner, Kristin and Palmer, Martha and Martin, James H.},editor={Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},month=may,year={2024},address={Torino, Italia},publisher={ELRA and ICCL},url={https://aclanthology.org/2024.lrec-main.920},pages={10517--10529},}
Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles
Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, George Arthur Baker, and
4 more authors
In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024
@inproceedings{nath-etal-2024-multimodal-cross,title={Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles},author={Nath, Abhijnan and Jamil, Huma and Ahmed, Shafiuddin Rehan and Baker, George Arthur and Ghosh, Rahul and Martin, James H. and Blanchard, Nathaniel and Krishnaswamy, Nikhil},editor={Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},month=may,year={2024},address={Torino, Italia},publisher={ELRA and ICCL},url={https://aclanthology.org/2024.lrec-main.1039},pages={11901--11916},}
X-AMR Annotation Tool
Shafiuddin Rehan Ahmed, Jon Cai, Martha Palmer, and James H. Martin
In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, Mar 2024
This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4.
@inproceedings{ahmed-etal-2024-x,title={{X}-{AMR} Annotation Tool},author={Ahmed, Shafiuddin Rehan and Cai, Jon and Palmer, Martha and Martin, James H.},editor={Aletras, Nikolaos and De Clercq, Orphee},booktitle={Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations},month=mar,year={2024},address={St. Julians, Malta},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2024.eacl-demo.19},pages={177--186},}
2023
CAMRA: Copilot for AMR Annotation
Jon Cai, Shafiuddin Rehan Ahmed, Julia Bonn, Kristin Wright-Bettner, and
2 more authors
In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Dec 2023
In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a cutting-edge web-based tool designed for constructing Abstract Meaning Representation (AMR) from natural language text. CAMRA offers a novel approach to deep lexical semantics annotation such as AMR, treating AMR annotation akin to coding in programming languages. Leveraging the familiarity of programming paradigms, CAMRA encompasses all essential features of existing AMR editors, including example lookup, while going a step further by integrating Propbank roleset lookup as an autocomplete feature within the tool. Notably, CAMRA incorporates AMR parser models as coding co-pilots, greatly enhancing the efficiency and accuracy of AMR annotators.
@inproceedings{cai-etal-2023-camra,title={{CAMRA}: Copilot for {AMR} Annotation},author={Cai, Jon and Ahmed, Shafiuddin Rehan and Bonn, Julia and Wright-Bettner, Kristin and Palmer, Martha and Martin, James H.},editor={Feng, Yansong and Lefever, Els},booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},month=dec,year={2023},address={Singapore},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2023.emnlp-demo.35},doi={10.18653/v1/2023.emnlp-demo.35},pages={381--388},}
How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?
Shafiuddin Rehan Ahmed, Abhijnan Nath, Michael Regan, Adam Pollins, and
2 more authors
In Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII), Jul 2023
Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97% recall while substantially reducing the workload required by a fully manual annotation process.
@inproceedings{ahmed-etal-2023-good,title={How Good Is the Model in Model-in-the-loop Event Coreference Resolution Annotation?},author={Ahmed, Shafiuddin Rehan and Nath, Abhijnan and Regan, Michael and Pollins, Adam and Krishnaswamy, Nikhil and Martin, James H.},booktitle={Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)},month=jul,year={2023},address={Toronto, Canada},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2023.law-1.14},doi={10.18653/v1/2023.law-1.14},pages={136--145},}
2*n is better than n^2: Decomposing Event Coreference Resolution into Two Tractable Problems
Shafiuddin Rehan Ahmed, Abhijnan Nath, James H. Martin, and Nikhil Krishnaswamy
In Findings of the Association for Computational Linguistics: ACL 2023, Jul 2023
Event Coreference Resolution (ECR) is the task of linking mentions of the same event either within or across documents. Most mention pairs are not coreferent, yet many that are coreferent can be identified through simple techniques such as lemma matching of the event triggers or the sentences in which they appear. Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult for the algorithm to learn coreference beyond surface matching. Additionally, these methods are intractable because of the quadratic operations needed. To address these challenges, we break the problem of ECR into two parts: a) a heuristic to efficiently filter out a large number of non-coreferent pairs, and b) a training approach on a balanced set of coreferent and non-coreferent mention pairs. By following this approach, we show that we get comparable results to the state of the art on two popular ECR datasets while significantly reducing compute requirements. We also analyze the mention pairs that are “hard” to accurately classify as coreferent or non-coreferentcode repo: \mathttgithub.com/ahmeshaf/lemma_ce_coref.
@inproceedings{ahmed-etal-2023-2,title={$2*n$ is better than $n^2$: Decomposing Event Coreference Resolution into Two Tractable Problems},author={Ahmed, Shafiuddin Rehan and Nath, Abhijnan and Martin, James H. and Krishnaswamy, Nikhil},booktitle={Findings of the Association for Computational Linguistics: ACL 2023},month=jul,year={2023},address={Toronto, Canada},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2023.findings-acl.100},doi={10.18653/v1/2023.findings-acl.100},pages={1569--1583},}
2020
Within-Document Event Coreference with BERT-Based Contextualized Representations
RAMFIS System Report TAC 2018.In Proceedings of the 2018 Text Analysis Conference, TAC 2018, Gaithersburg, Maryland, USA, November 13-14, 2018, Jul 2018
