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Found 14 papers, 3 papers with code
More Victories, Less Cooperation: Assessing Cicero's Diplomacy Play
The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence.
The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages
Efficiently and accurately translating a corpus into a low-resource language remains a challenge, regardless of the strategies employed, whether manual, automated, or a combination of the two.
User Study for Improving Tools for Bible Translation
Technology has increasingly become an integral part of the Bible translation process.
AMR Beyond the Sentence: the Multi-sentence AMR corpus
There are few corpora that endeavor to represent the semantic content of entire documents.
Translating a Language You Don't Know In the Chinese Room
In a corruption of John Searle{'}s famous AI thought experiment, the Chinese Room (Searle, 1980), we twist its original intent by enabling humans to translate text, e. g. from Uyghur to English, even if they don{'}t have any prior knowledge of the source language.
Out-of-the-box Universal Romanization Tool uroman
We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation.
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations.
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling.
