The ERG at MRP 2019: Radically Compositional Semantic Dependencies

Abstract

The English Resource Grammar (ERG) is a broad-coverage computational grammar of English that derives underspecified logicalform representations of meaning. Elementary Dependency Structures (EDS) and DELPH-IN MRS Bi-Lexical Dependencies (DM) are graph-based simplifications of ERG meaning representations. As a point of reference outside the official competition of the 2019 Shared Task on Cross-Framework Meaning Representation Parsing, we evaluate ERGderived EDS and DM graphs. These graphs yield higher accuracy scores than the purely data-driven parsers in the shared task, suggesting that the general-purpose grammatical knowledge encoded in the ERG aids parsing into these meaning representations.

MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

“…In addition, we received two submissions after the submission deadline, which we mark as 'unofficial'. We further include results from an additional 'reference' system by one of the task co-organizers, namely EDS outputs from the grammar-based ERG parser (Oepen and Flickinger, 2019).…”

“…Both used factorization-based parsing with pre-trained contextualized language model embeddings (which has consistently proved to be very effective for other frameworks too). These parsers even approached the performance of the carefully designed grammarbased ERG parser (Oepen and Flickinger, 2019). English PTG has not been comprehensively addressed by parsers prior to MRP 2020, but a bilexical framework called PSD is a subset of PTG.…”

MRP 2019: Cross-Framework Meaning Representation Parsing

“…The task received submissions from sixteen teams, plus another two 'reference' submissions prepared by the task co-organizers (Hershcovich and Arviv, 2019;Oepen and Flickinger, 2019). These reference points are not considered in the overall ranking.…”

JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing

“…Given the preliminary results, we chose the basic biaffine model "BERT+Biaffine" of Table 3 for the final submission to MRP 2019. The official results using BERT+Biaffine are summarized in Tables 4 and 5, which compare the results of ERG (Oepen and Flickinger, 2019) and TUPA (Hershcovich and Arviv, 2019) which were provided by the task organizer. Table 4 shows the performances of the MRP metrics on the three frameworks, whereas Table 5 presents the performances of task-specific metrics using the SDM metrics and UCCA metric .…”

“…In addition, we would like to examine alternative fusion functions for multi-level affine attention. (Oepen and Flickinger, 2019), TUPA (Hershcovich and Arviv, 2019), and our system (BERT+Biaffine). (Oepen and Flickinger, 2019), TUPA (Hershcovich and Arviv, 2019), and our system (BERT+Biaffine).…”