AMR dependency parsing with a typed semantic algebra

Groschwitz, Jonas; Lindemann, Matthias; Fowlie, Meaghan; Johnson, Mark; Koller, Alexander

doi:10.18653/v1/p18-1170

Cited by 63 publications

(107 citation statements)

References 36 publications

(50 reference statements)

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“…Corpus Parser F1(%) AMR 2.0 Buys and Blunsom (2017) 61.9 van Noord and Bos (2017b) 71.0 * Groschwitz et al (2018) 71.0±0.5 Lyu and Titov (2018) 74.4±0.2 Naseem et al (2019) 75.5…”

Section: Resultsmentioning

confidence: 99%

“…(2) the lack of gold alignments between nodes (concepts) in the graph and words in the text which limits attempts to rely on explicit alignments to generate training data (Flanigan et al, 2014;Wang et al, 2015;Damonte et al, 2017;Foland and Martin, 2017;Peng et al, 2017b;Groschwitz et al, 2018;Guo and Lu, 2018); and (3) relatively limited amounts of labeled data (Konstas et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AMR Parsing as Sequence-to-Graph Transduction

Zhang¹,

Ma²,

Duh³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

109

134

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We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0 (70.2% F1 on LDC2014T12). Another View of ReentrancyAMR is a rooted, directed, and usually acyclic graph where nodes represent concepts, and labeled directed edges represent the relationships between them (see Figure 1 for an AMR example). The reason for AMR being a graph instead of a tree is that it allows reentrant semantic relations. For instance, in Figure 1(a) "victim" is both ARG0 and arXiv:1905.08704v2 [cs.CL]

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“…Corpus Parser F1(%) AMR 2.0 Buys and Blunsom (2017) 61.9 van Noord and Bos (2017b) 71.0 * Groschwitz et al (2018) 71.0±0.5 Lyu and Titov (2018) 74.4±0.2 Naseem et al (2019) 75.5…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AMR Parsing as Sequence-to-Graph Transduction

Zhang¹,

Ma²,

Duh³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

109

134

View full text Add to dashboard Cite

show abstract

“…2 Since the size of AMR graph is approximately linear in the length of sentence. 2015; Foland and Martin, 2017;Lyu and Titov, 2018;Groschwitz et al, 2018;Guo and Lu, 2018). The hand-crafted rules for re-categorization are often non-trivial, requiring exhaustive screening and expert-level manual efforts.…”

Section: Introductionmentioning

confidence: 99%

Core Semantic First: A Top-down Approach for AMR Parsing

Cai

Lam

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.

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“…Table 4 shows the SEMBLEU and SMATCH scores several recent models. In particular, we asked for the outputs of Lyu (Lyu and Titov, 2018), Gros (Groschwitz et al, 2018), van Nood (van Noord and Bos, 2017) and Guo (Guo and Lu, 2018) to evaluate on our SEMBLEU. For CAMR and JAMR, we obtain their outputs by running the released systems.…”

Section: Sentence-level Experimentsmentioning

confidence: 99%

“…Despite the large amount of work on AMR parsing (Flanigan et al, 2014;Artzi et al, 2015;Pust et al, 2015;Peng et al, 2015;Buys and Blunsom, 2017;Konstas et al, 2017;Wang and Xue, 2017;Ballesteros and Al-Onaizan, 2017;Lyu and Titov, 2018;Peng et al, 2018;Groschwitz et al, 2018;Guo and Lu, 2018), little attention has been paid to evaluating the parsing results, leaving SMATCH Figure 2: Average, minimal and maximal SMATCH scores over 100 runs on 100 sentences. The running time increases from 6.6 seconds (r=1) to 21.0 (r = 4).…”

Section: Introductionmentioning

confidence: 99%

SemBleu: A Robust Metric for AMR Parsing Evaluation

Song

Gildea

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH , searches for one-to-one mappings between the nodes of two AMRs with a greedy hill-climbing algorithm, which leads to search errors. We propose SEMBLEU, a robust metric that extends BLEU (Papineni et al., 2002) to AMRs. It does not suffer from search errors and considers non-local correspondences in addition to local ones. SEMBLEU is fully content-driven and punishes situations where a system's output does not preserve most information from the input. Preliminary experiments on both sentence and corpus levels show that SEMBLEU has slightly higher consistency with human judgments than SMATCH. Our code is available at http://github.com/ freesunshine0316/sembleu.

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AMR dependency parsing with a typed semantic algebra

Cited by 63 publications

References 36 publications

AMR Parsing as Sequence-to-Graph Transduction

AMR Parsing as Sequence-to-Graph Transduction

Core Semantic First: A Top-down Approach for AMR Parsing

SemBleu: A Robust Metric for AMR Parsing Evaluation

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