Papers with structured meaning representations
EUSP: An Easy-to-Use Semantic Parsing PlatForm (D19-3)
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| Challenge: | Semantic parsing aims to map natural language utterances into structured meaning representations. |
| Approach: | They propose a modular platform that allows developers to build semantic parser from scratch. |
| Outcome: | The proposed platform achieves competitive performance on semantic parsing task and improves performance of a business search engine. |
Coarse-to-Fine Decoding for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that semantic parsing is more efficient than using simple decoders. |
| Approach: | They propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. |
| Outcome: | The proposed architecture consistently improves performance on four datasets characteristic of different domains and meaning representations. |
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations (2022.naacl-main)
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| Challenge: | Recent studies show that abstractive summarization approaches generate summaries that are not factually consistent with the source document. |
| Approach: | They propose a method that decomposes the document and summary into structured meaning representations (MRs) MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form . |
| Outcome: | The proposed method outperforms existing methods on benchmarks for factuality evaluation. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |
Pragmatically Informative Text Generation (N19-1)
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| Challenge: | Existing approaches to pragmatics have been used to improve the informativeness of generated text in grounded language learning problems. |
| Approach: | They propose to use pragmatics to improve the informativeness of conditional text models . they propose to apply pragmatic reasoning to more traditional language generation tasks . |
| Outcome: | The proposed methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations. |
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)
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| Challenge: | Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates. |
| Approach: | They propose a method for generating a corpus of parallel meaning representations with rich style markup using freely available and naturally descriptive user reviews. |
| Outcome: | The proposed method can be scalably reused to generate NLG datasets for other domains. |