Papers by Stephen Tratz
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
A Web-based System for Crowd-in-the-Loop Dependency Treebanking (L18-1)
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| Challenge: | Existing treebanks are limited in size, genre, and topic coverage, making manual annotation time-consuming and expensive. |
| Approach: | They propose a web-based interactive tool for editing dependency trees that uses machine learning to accelerate annotation. |
| Outcome: | CROWDTREE is a web-based interactive tool for editing dependency trees . it can train a parsing model during the annotation process and can even be compatible with Mechanical Turk. |
Dependency Tree Annotation with Mechanical Turk (D19-59)
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| Challenge: | a recent study shows that crowdsourcing is often used to obtain linguistic annotations but is rarely used for parsing. |
| Approach: | They propose to use Mechanical Turk to crowdsource parse trees using an interactive graphical dependency tree editor. |
| Outcome: | The proposed method is the first published use of Mechanical Turk to crowdsource parse trees . the authors find that the workers achieve high levels of accuracy on 72% of the sentences . |