Papers by Kevin Parnow
Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)
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| Challenge: | Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems . |
| Approach: | They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors. |
| Outcome: | The proposed model improves the state-of-the-art in GEC and improves on benchmarks. |
MiSS: An Assistant for Multi-Style Simultaneous Translation (2021.emnlp-demo)
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| Challenge: | MiSS is a multi-style simultaneous translation assistant . it has five key features: high translation accuracy, simultaneous translation, flexibility, and measurable translation quality. |
| Approach: | They propose an assistant system for multi-style simultaneous translation that provides a complete translation experience for machine translation users. |
| Outcome: | The proposed system improves translation efficiency and performance by combining machine translation, grammatical error correction, and interactive edits. |
High-order Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Experimental results show that high-order structural learning techniques are beneficial to SRL models . high-level features and structure learning are not common in deep neural networks . |
| Approach: | They propose a high-order graph structure for a neural semantic role labeling model . it explicitly considers the isolated predicate-argument pairs and interaction between them . |
| Outcome: | The proposed model can explicitly consider the isolated predicate-argument pairs and the interaction between the predicates-argoments pairs. |