Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking. Abstract Meaning Representation (AMR) represents text as local knowledge graphs. Evaluating the quality of these graphs involves matching them structurally to each other and semantically to the source text. Existing AMR metrics are inefficient and struggle to capture semantic similarity. We also lack a systematic evaluation benchmark for assessing structural similarity between AMR graphs. To overcome these limitations, we introduce a novel AMR similarity metric, rematch, alongside a new evaluation for structural similarity called RARE. Among state-of-the-art metrics, rematch ranks second in structural similarity; and first in semantic similarity by 1–5 percentage points on the STS-B and SICK-R benchmarks. Rematch is also five times faster than the next most efficient metric.
@inproceedings{kachwala2024rematch,title={Rematch: Robust and Efficient Matching of Local Knowledge Graphs for Improved Structural and Semantic Similarity},author={Kachwala, Zoher and An, Jisun and Kwak, Haewoon and Menczer, Filippo},booktitle={2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics},year={2024},url={https://openreview.net/forum?id=dBnsZ72qUQ},eprint={https://arxiv.org/abs/2404.02126}}
2023
A multi-platform collection of social media posts about the 2022 US midterm elections
Rachith Aiyappa , Matthew R DeVerna , Manita Pote , and 8 more authors
In Proceedings of the International AAAI Conference on Web and Social Media , 2023
Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.
@inproceedings{aiyappa2023multi,title={A multi-platform collection of social media posts about the 2022 US midterm elections},author={Aiyappa, Rachith and DeVerna, Matthew R and Pote, Manita and Truong, Bao Tran and Zhao, Wanying and Axelrod, David and Pessianzadeh, Aria and Kachwala, Zoher and Kim, Munjung and Seckin, Ozgur Can and others},booktitle={Proceedings of the International AAAI Conference on Web and Social Media},volume={17},pages={981--989},year={2023},doi={10.1609/icwsm.v17i1.22205},url={https://ojs.aaai.org/index.php/ICWSM/article/view/22205},}
The Inexplicable Efficacy of Language Models
Rachith Aiyappa , and Zoher Kachwala
XRDS: Crossroads, The ACM Magazine for Students, Apr 2023
A brief insight into language modelling – a statistical way of teaching language to machines, and its inexplicable capablities. It also highlights a few shortcomings of such models and discusses some approacheds to address them.
@article{aiyappa_inexplicable_2023,title={The {Inexplicable} {Efficacy} of {Language} {Models}},volume={29},issn={1528-4972},url={https://dl.acm.org/doi/10.1145/3589654},doi={10.1145/3589654},number={3},urldate={2024-03-18},journal={XRDS: Crossroads, The ACM Magazine for Students},author={Aiyappa, Rachith and Kachwala, Zoher},month=apr,year={2023},pages={60--62},}
