AMR Parsing as Graph Prediction with Latent Alignment

Lyu, Chunchuan; Titov, Ivan

doi:10.48550/arxiv.1805.05286

Cited by 6 publications

(11 citation statements)

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“…In order to produce Wikipedia entries in our AMR graphs, we run a wikification approach as post-processing. We combine the approach of Lyu and Titov (2018) with the entity linking technique of Sil et al (2018).…”

Section: Wikificationmentioning

confidence: 99%

“…During post processing, every node with :name label is looked up in the dictionary and if found, is assigned the corresponding Wikipedia link. This is very similar to the approach of Lyu and Titov (2018). If the node is not found in the dictionary, and the system of Sil et al (2018) produces a Wikipedia link, we use that link.…”

Section: Wikificationmentioning

confidence: 99%

“…AMR parsing thus requires solving several natural language processing tasks; named entity recognition, word sense disambiguation and joint syntactic and semantic role labeling. AMR parsing has acquired a lot of attention (Wang et al, 2015a;Zhou et al, 2016;Wang et al, 2015b;Goodman et al, 2016;Guo and Lu, 2018;Lyu and Titov, 2018; Vilares and Gómez-Rodríguez, 2018; Zhang et al, 2019) in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Naseem¹,

Shah²,

Wan³

et al. 2019

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

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Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser.

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Section: Wikificationmentioning

confidence: 99%

Section: Wikificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Naseem¹,

Shah²,

Wan³

et al. 2019

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

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“…They also model inductive biases indirectly through graph re-categorization, detailed in Section 6.1, which requires a name entity recognition system at test time. Re-categorization was proposed in Lyu and Titov (2018), which reformulated alignments as a differentiable permutation problem, interpretable as another form of inductive bias.…”

Section: Related Workmentioning

confidence: 99%

AMR Parsing with Action-Pointer Transformer

Zhou

Naseem²,

Astudillo³

et al. 2021

Preprint

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Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a targetside action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best SMATCH score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.

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“…Fundamentally, this is because predicting graphs is difficult: every graph has many possible linearizations, so from a probabilistic perspective, the linearization is a latent variable that must be marginalized out (Li et al, 2018). Groschwitz et al (2018) model graphs as trees, interpreted as the (latent) derivation trees of a graph grammar; Lyu and Titov (2018) model graphs with a conditional variant of the classic Erdös and Rényi (1959) model, first predicting an alignment for each node of the output graph, and then predicting, for each pair of nodes, whether there is an edge between them. Buys and Blunsom (2017), Chen et al (2018), and Damonte et al (2017) all model graph generation as a sequence of actions, each aligned to a word in the conditioning sentence.…”

Section: Introductionmentioning

confidence: 99%

Semantic graph parsing with recurrent neural network DAG grammars

Fancellu¹,

Gilroy²,

Lopez³

et al. 2019

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

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Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the linearized graphs found in semantic parsing datasets using well-understood sequence models. The cost of this simplicity is that the predicted strings may not be wellformed graphs. We present recurrent neural network DAG grammars, a graph-aware sequence model that ensures only well-formed graphs while sidestepping many difficulties in graph prediction. We test our model on the Parallel Meaning Bank-a multilingual semantic graphbank. Our approach yields competitive results in English and establishes the first results for German, Italian and Dutch.

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AMR Parsing as Graph Prediction with Latent Alignment

Cited by 6 publications

References 0 publications

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

AMR Parsing with Action-Pointer Transformer

Semantic graph parsing with recurrent neural network DAG grammars

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