Papers by Ulf Hermjakob
AMR Beyond the Sentence: the Multi-sentence AMR corpus (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is limited to capturing the semantics of individual sentences. |
| Approach: | They propose a corpus that annotates coreference and similar phenomena on top of existing AMRs. |
| Outcome: | The proposed corpus is compared with existing corpora on sentence-level semantics . it shows that it can be used for information extraction and question answering . |
Out-of-the-box Universal Romanization Tool uroman (P18-4)
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| Challenge: | uroman converts text in Chinese, Arabic and Cyrillic into a common Latin-script representation . the tool uses string similarity metrics to compare text from different scripts . |
| Approach: | They propose a tool that converts text in Chinese, Arabic and Cyrillic into a common Latin-script representation. |
| Outcome: | uroman converts text in Chinese, Arabic and Cyrillic into a common Latin-script representation . the tool is available as a Perl script and as an interactive demo web page . |
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
| Approach: | They propose to expand the AMR project's lexicon of predicate senses to include entries for a growing set of constructions. |
| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
Translating a Language You Don’t Know In the Chinese Room (P18-4)
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| Challenge: | In a corruption of John Searle’s famous AI thought experiment, the Chinese Room, we enable humans to translate text even if they don’t have any prior knowledge of the source language. |
| Approach: | They propose a tool that allows humans to translate text without prior knowledge of the source language. |
| Outcome: | The Chinese Room tool can create fluent translations with human expertise required only for the target language. |
More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play (2024.acl-long)
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Wichayaporn Wongkamjan, Feng Gu, Yanze Wang, Ulf Hermjakob, Jonathan May, Brandon Stewart, Jonathan Kummerfeld, Denis Peskoff, Jordan Boyd-Graber
| Challenge: | Diplomacy is a boardgame that offers a challenge for communicative and cooperative AI. |
| Approach: | They run two dozen games with Cicero and annotate in-game communication with abstract meaning representation to separate in- game tactics from general language. |
| Outcome: | The proposed method can outperform Cicero in communicating with humans, but it's difficult to deceive and persuade AI. |