End-to-End AMR Corefencence Resolution

Fu, Qiankun; Song, Linfeng; Du, Wei; Zhang, Yue

doi:10.18653/v1/2021.acl-long.324

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…The best approach from their study is incorporated as a baseline in §5. Fu et al (2021) introduce an AMR coreference resolution system that uses graph neural network to model gold sentencelevel AMR graphs for coreference predictions. This system assumes gold graphs and is not comparable with document-level parsing systems.…”

Section: Resultsmentioning

confidence: 99%

DocAMR: Multi-Sentence AMR Representation and Evaluation

Naseem¹,

Blodgett²,

Kumaravel³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Despite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, full-document parsing into a unified graph representation lacks welldefined representation and evaluation. Taking advantage of a super-sentential level of AMR coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information loss from over-merging and lack of coherence from under-merging. Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs, and use it to re-evaluate the best published document-level AMR parser. We also present a pipeline approach combining the top performing AMR parser and coreference resolution systems, providing a strong baseline for future research.

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Section: Resultsmentioning

confidence: 99%

DocAMR: Multi-Sentence AMR Representation and Evaluation

Naseem¹,

Blodgett²,

Kumaravel³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…In particular, we explore Abstract Meaning Representation (AMR) (Banarescu et al, 2013), a semantic formalism that has received much research interest (Song et al, 2018;Guo et al, 2019;Ribeiro et al, , 2021aOpitz et al, 2020Opitz et al, , 2021Fu et al, 2021) and has been shown to benefit downstream tasks such as spoken language understanding (Damonte et al, 2019), machine translation (Song et al, 2019), commonsense reasoning (Lim et al, 2020), and question answering (Kapanipathi et al, 2021;Bornea et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

Ribeiro¹,

Liu²,

Gurevych³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications. Recent works have shown promising improvements in factuality error identification using text or dependency arc entailments; however, they do not consider the entire semantic graph simultaneously. To this end, we propose FACT-GRAPH, a method that decomposes the document and the summary into structured meaning representations (MR), which are more suitable for factuality evaluation. MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity. FACTGRAPH encodes such graphs using a graph encoder augmented with structure-aware adapters to capture interactions among the concepts based on the graph connectivity, along with text representations using an adapter-based text encoder. Experiments on different benchmarks for evaluating factuality show that FACTGRAPH outperforms previous approaches by up to 15%. Furthermore, FACT-GRAPH improves performance on identifying content verifiability errors and better captures subsentence-level factual inconsistencies. 1 * Work done as an intern at Amazon Alexa AI.

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Section: Introductionmentioning

confidence: 99%

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

Ribeiro¹,

Liu²,

Gurevych³

et al. 2022

Preprint

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Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications. Recent works have shown promising improvements in factuality error identification using text or dependency arc entailments; however, they do not consider the entire semantic graph simultaneously. To this end, we propose FACTGRAPH, a method that decomposes the document and the summary into structured meaning representations (MR), which are more suitable for factuality evaluation. MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity. FACTGRAPH encodes such graphs using a graph encoder augmented with structureaware adapters to capture interactions among the concepts based on the graph connectivity, along with text representations using an adapter-based text encoder. Experiments on different benchmarks for evaluating factuality show that FACTGRAPH outperforms previous approaches by up to 15%. Furthermore, FACT-GRAPH improves performance on identifying content verifiability errors and better captures subsentence-level factual inconsistencies. 1 * Work done as an intern at Amazon Alexa AI.

show abstract

End-to-End AMR Corefencence Resolution

Cited by 12 publications

References 35 publications

DocAMR: Multi-Sentence AMR Representation and Evaluation

DocAMR: Multi-Sentence AMR Representation and Evaluation

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

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