ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

Abstract

Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g. MT and summarization in the text-to-AMR transition even with much less data. 2) To make a better fit for AMR, data from auxiliary tasks should be properly "AM-Rized" to PseudoAMR before training. Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform. 3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning. From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-ofthe-art performance on different benchmarks especially in topology-related scores. Code and models are released at https://github. com/PKUnlp-icler/ATP.

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“…Two AMR-parsing models are currently the non-ensemble SotA: AMRBART (Bai et al, 2022) and ATP (Chen et al, 2022). These are closely followed by SPRING (Bevilacqua et al, 2021), the https://github.com/blazegraph/database https://solr.apache.org/…”

Addressing Long-Distance Dependencies in AMR Parsing with Hierarchical Clause Annotation

“…Therefore, we also choose SRPING as the baseline model to apply our HCAbased approaches. Additionally, we do not take the competitive AMR parser, ATP [19], into consideration for our compared models since it employs syntactic dependency parsing and semantic role labeling as intermediate tasks to introduce extra silver training data.…”

“…In seq2seq-based approaches, Bevilacqua et al [15] employ the Transformer-based pretrained language model, BART [16], to address LDDs in long sentences. Among these categories, seq2seq-based approaches have become mainstream, and recent parsers [17][18][19][20] employ the seq2seq architecture with the popular codebase SPRING [15], achieving better performance. Notably, HGAN [20] integrates token-level features, syntactic dependencies (SDP), and SRL with heterogeneous graph neural networks and has become the state-ofthe-art (SOTA) in terms of removing extra silver training data, graph-categorization, and ensemble methods.…”

“…Seq2seq-based: Model the task as transduction of the sentence into a linearization of the AMR graph [15,[17][18][19][20][35][36][37][38]. SPRING [15] is a popular seq2seq-based codebase that employs transfer learning by exploiting a pretrained encoder-decoder model, BART [16], to generate a linearized graph incrementally with a single auto-regressive pass of a seq2seq decoder.…”

“…SPRING [15] is a popular seq2seq-based codebase that employs transfer learning by exploiting a pretrained encoder-decoder model, BART [16], to generate a linearized graph incrementally with a single auto-regressive pass of a seq2seq decoder. The subsequent models, ANCES [17], HCL [18], ATP [19], and HGAN [20] all follow the architecture of SPRING, and HGAN integrates SDP and SRL features with heterogeneous graph neural networks to achieve the best performance in the tasks of removing extra silver training data, graph re-categorization, and ensemble methods.…”