| Challenge: | Semantic parsing (SP) maps a natural language utterance into a formal language . standard Seq2Seq models ignore underlying grammars and may give ill-formed results. |
| Approach: | They propose an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. |
| Outcome: | The proposed model outperforms the state-of-the-art models and does not need expertise like predefined grammar or sketches in the meantime. |
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| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
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. |
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (2022.findings-acl)
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| Challenge: | Recent work in task-independent graph semantic parsing has shifted from symbolic approaches to neural models, showing strong performance on different types of meaning representations. |
| Approach: | They propose a framework that incorporates prior knowledge from a symbolic parser into a decision criterion for beam search to address these limitations. |
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Confidence Modeling for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that neural semantic parsers are difficult to interpret due to their complexity. |
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| Outcome: | The proposed model outperforms a widely used method that relies on posterior probability and improves interpretation quality. |
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing methods for neural machine translation only observe one source sentence at training time . this discrepancy in data distribution leads to a formidable learning challenge . |
| Approach: | They propose an uncertainty-aware semantic augmentation approach to capture universal semantic information among multiple source sentences and enhance hidden representations with this information. |
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Grammar-Constrained Neural Semantic Parsing with LR Parsers (2021.findings-acl)
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| Challenge: | a context-free grammar can be used to enforce syntactical constraints when predicting logical forms. |
| Approach: | They propose a model that uses an LR parser to maintain syntactically valid sequences throughout the decoding procedure. |
| Outcome: | The proposed model is conceptually simpler and adds less overhead during inference compared to other approaches . it is compared with existing grammar-guided decoding frameworks and is cost-effective . |
Inducing and Using Alignments for Transition-based AMR Parsing (2022.naacl-main)
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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo
| Challenge: | Abstract Meaning Representation parsers rely on node-to-word alignments, but lack the complexity of the pipeline. |
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Guiding Neural Machine Translation with Semantic Kernels (2022.findings-emnlp)
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| Challenge: | Empirical studies show that our approach gains approximately an improvement of 1 BLEU score on most benchmarks over the Transformer baseline. |
| Approach: | They propose to extract several semantic kernels from a source sentence to capture global semantic information. |
| Outcome: | Empirical results show that the proposed approach improves 1 BLEU score on benchmarks . it is also 1.7 times faster than previous works on average at inference time . |
Modeling Input Uncertainty in Neural Network Dependency Parsing (D18-1)
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| Challenge: | Recent advances in neural network parsers address data sparsity issues by modeling character level information and exploiting raw data in semi-supervised settings. |
| Approach: | They investigate whether lexical normalization provides similar functionality to lexiconal normalization . they show that a separate normalization component improves performance of a neural network parser . |
| Outcome: | The proposed approaches improve performance even with access to character level information and word embeddings. |
Compositional Generalization via Semantic Tagging (2021.findings-emnlp)
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| Challenge: | Existing neural sequence-to-sequence models fail at compositional generalization, i.e., they cannot generalize to unseen compositions of seen components. |
| Approach: | They propose a decoding framework that preserves expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. |
| Outcome: | The proposed framework improves compositional generalization across model architectures, domains, and semantic formalisms on three semantic parsing datasets. |