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of 8
pro vyhledávání: '"Vashishtha, Siddharth"'
Autor:
Vashishtha, Siddharth, Martin, Alexander, Gantt, William, Van Durme, Benjamin, White, Aaron Steven
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event \emph{across documents} can offer a much richer understa
Externí odkaz:
http://arxiv.org/abs/2311.05601
Autor:
Barham, Samuel, Weller, Orion, Yuan, Michelle, Murray, Kenton, Yarmohammadi, Mahsa, Jiang, Zhengping, Vashishtha, Siddharth, Martin, Alexander, Liu, Anqi, White, Aaron Steven, Boyd-Graber, Jordan, Van Durme, Benjamin
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process t
Externí odkaz:
http://arxiv.org/abs/2307.07049
Autor:
Goel, Rahul, Ammar, Waleed, Gupta, Aditya, Vashishtha, Siddharth, Sano, Motoki, Surani, Faiz, Chang, Max, Choe, HyunJeong, Greene, David, He, Kyle, Nitisaroj, Rattima, Trukhina, Anna, Paul, Shachi, Shah, Pararth, Shah, Rushin, Yu, Zhou
Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academic research in this area has been limited by the lack of datasets that
Externí odkaz:
http://arxiv.org/abs/2303.08954
As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE. In this position paper, we call into question the suitabili
Externí odkaz:
http://arxiv.org/abs/2212.09702
Autor:
Xia, Patrick, Qin, Guanghui, Vashishtha, Siddharth, Chen, Yunmo, Chen, Tongfei, May, Chandler, Harman, Craig, Rawlins, Kyle, White, Aaron Steven, Van Durme, Benjamin
We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs
Externí odkaz:
http://arxiv.org/abs/2101.12175
The task of Semantic Parsing can be approximated as a transformation of an utterance into a logical form graph where edges represent semantic roles and nodes represent word senses. The resulting representation should be capture the meaning of the utt
Externí odkaz:
http://arxiv.org/abs/2006.15942
Autor:
White, Aaron Steven, Stengel-Eskin, Elias, Vashishtha, Siddharth, Govindarajan, Venkata, Reisinger, Dee Ann, Vieira, Tim, Sakaguchi, Keisuke, Zhang, Sheng, Ferraro, Francis, Rudinger, Rachel, Rawlins, Kyle, Van Durme, Benjamin
We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification
Externí odkaz:
http://arxiv.org/abs/1909.13851
We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the
Externí odkaz:
http://arxiv.org/abs/1902.01390