Papers with semantic parses
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention (2021.naacl-main)
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Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, Jacob Andreas
| Challenge: | Existing approaches to compositional generalization in semantic parsers focus on word-level alignments, but they focus on spans. |
| Approach: | They propose a span-level supervised attention loss that improves compositional generalization in semantic parsers by focusing on spans. |
| Outcome: | The proposed method improves on three benchmarks of compositional generalization. |
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing (2022.acl-long)
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| Challenge: | Semantic parsers struggle to generalize to examples with unseen combinations of seen rules from the training set. |
| Approach: | They propose a general framework to produce semantic parses by predicting node labels for a complete multi-layer input-aligned graph. |
| Outcome: | The proposed framework produces better generalizations than the baseline framework . it produces representations directly as a graph and not as sequences . |
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations (D18-1)
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| Challenge: | Existing learning approaches for parsing from denotations (SpFD) do not provide access to correct representations, so there are two steps for every training example. |
| Approach: | They propose a framework for parsing from denotations that generalizes three different learning algorithms. |
| Outcome: | The proposed framework outperforms previous work by 5.0% absolute on exact match accuracy on a question answering dataset. |
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering (C18-1)
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| Challenge: | Existing approaches to Knowledge Base Question Answering focus on semantic parsing . previous work focused on selecting the correct semantic relations and not on the structure of the semantic parses . |
| Approach: | They propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. |
| Outcome: | The proposed approach outperforms baseline models that do not explicitly model the structure. |
Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)
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| Challenge: | Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency. |
| Approach: | They propose a framework for logic consistent text generation from semantic parses that employs iterative training procedures and quality control. |
| Outcome: | The proposed framework enhances logic consistency and human evaluation on two benchmark datasets. |
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding (2024.findings-emnlp)
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Trang Le, Daniel Lazar, Suyoun Kim, Shan Jiang, Duc Le, Adithya Sagar, Aleksandr Livshits, Ahmed Aly, Akshat Shrivastava
| Challenge: | End-to-end models for Spoken Language Understanding have been autoregressive, resulting in higher latencies. |
| Approach: | They propose a method that uses Connectionist Temporal Classification to train robust non-autoregressive deliberation models. |
| Outcome: | The proposed method achieves 10x latency reduction over autoregressive models while preserving ability to correct ASR mistranscriptions. |