An AMR parser for English, French, German, Spanish and Japanese and a new AMR-annotated corpus

Abstract

In this demonstration, we will present our online parser 1 that allows users to submit any sentence and obtain an analysis following the specification of AMR (Banarescu et al., 2014) to a large extent. This AMR analysis is generated by a small set of rules that convert a native Logical Form analysis provided by a preexisting parser (see Vanderwende, 2015) into the AMR format. While we demonstrate the performance of our AMR parser on data sets annotated by the LDC, we will focus attention in the demo on the following two areas: 1) we will make available AMR annotations for the data sets that were used to develop our parser, to serve as a supplement to the LDC data sets, and 2) we will demonstrate AMR parsers for German, French, Spanish and Japanese that make use of the same small set of LF-to-AMR conversion rules. IntroductionAbstract Meaning Representation (AMR) (Banarescu et al., 2014) is a semantic representation for which a large amount of manually-annotated data is being created, with the intent of constructing and evaluating parsers that generate this level of semantic representation for previously unseen text.1 Available at: http://research.microsoft.com/msrsplat Already one method for training an AMR parser has appeared in (Flanigan et al., 2014), and we anticipate that more attempts to train parsers will follow. In this demonstration, we will present our AMR parser, which converts our existing semantic representation formalism, Logical Form (LF), into the AMR format. We do this with two goals: first, as our existing LF is close in design to AMR, we can now use the manually-annotated AMR datasets to measure the accuracy of our LF system, which may serve to provide a benchmark for parsers trained on the AMR corpus. We gratefully acknowledge the contributions made by Banarescu et al. (2014) towards defining a clear and interpretable semantic representation that enables this type of system comparison. Second, we wish to contribute new AMR data sets comprised of the AMR annotations by our AMR parser of the sentences we previously used to develop our LF system. These sentences were curated to cover a widerange of syntactic-semantic phenomena, including those described in the AMR specification. We will also demonstrate the capabilities of our parser to generate AMR analyses for sentences in French, German, Spanish and Japanese, for which no manually-annotated AMR data is available at present. Abstract Meaning RepresentationAbstract Meaning Representation (AMR) is a semantic representation language which aims to assign the same representation to sentences that have 26

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