Papers by Siddharth Vashishtha
Fine-Grained Temporal Relation Extraction (P19-1)
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| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)
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Rahul Goel, Waleed Ammar, Aditya Gupta, Siddharth Vashishtha, Motoki Sano, Faiz Surani, Max Chang, HyunJeong Choe, David Greene, Chuan He, Rattima Nitisaroj, Anna Trukhina, Shachi Paul, Pararth Shah, Rushin Shah, Zhou Yu
| Challenge: | PRESTO dataset contains 550K contextual multilingual conversations between humans and virtual assistants. |
| Approach: | They propose to use a dataset of 550K contextual multilingual conversations between humans and virtual assistants to study some of the more challenging aspects of parsing realistic conversations. |
| Outcome: | The dataset contains 550K contextual conversations between humans and virtual assistants. |
FAMuS: Frames Across Multiple Sources (2024.naacl-long)
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| Challenge: | Recent work in document-level event and argument extraction tasks suffer from two key shortcomings. |
| Approach: | They propose to combine Wikipedia passages with underlying, genre-diverse source articles for an event . they propose two key task enabled by FAMuS: source validation and cross-document argument extraction . |
| Outcome: | The proposed system can extract event arguments from document and report documents. |
LOME: Large Ontology Multilingual Extraction (2021.eacl-demos)
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Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme
| Challenge: | LOME is a system for performing multilingual information extraction with large ontologies. |
| Approach: | They propose a system for multilingual information extraction with a framenet parser . LOME is available as a Docker container on Docker Hub and a lightweight version is available on the web . |
| Outcome: | The proposed system outperforms or is competitive with the (monolingual) state-of-the-art . it can be used to build knowledge graphs with large ontologies and across multiple languages . |
On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)
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| Challenge: | a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police . |
| Approach: | They argue that the task demands definitive answers to thorny questions of *event individuation* they argue that even human experts disagree on the task . |
| Outcome: | The proposed task demands definitive answers to thorny questions of *event individuation* . the proposed task also raises concerns about the usefulness of template filling metrics . |
Temporal Reasoning in Natural Language Inference (2020.findings-emnlp)
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| Challenge: | We use five new natural language inference (NLI) datasets focused on temporal reasoning. |
| Approach: | They introduce five new natural language inference datasets focused on temporal reasoning. |
| Outcome: | The proposed models capture the temporal reasoning of four existing datasets. |
The Universal Decompositional Semantics Dataset and Decomp Toolkit (2020.lrec-1)
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Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
| Challenge: | Decompositional semantics is a method of crowd-sourcing semantic annotations while retaining high interannotator agreement. |
| Approach: | They present the Universal Decompositional Semantics dataset (v1.0) they propose a decomposition-aligned approach to semantic annotation that uses simple questions to answer . |
| Outcome: | The dataset is bundled with the Decomp toolkit (v0.1) both datasets are publicly available at http://decomp.io. |