| Challenge: | Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. |
| Approach: | They propose a score-based method to select well-formed outputs from candidates generated by beam search algorithms. |
| Outcome: | The proposed method reduces the number of ill-formed outputs and improves F1 scores in English. |
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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. |
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT (2020.emnlp-main)
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| Challenge: | a new method of analysis based on semantic tags demonstrates that character-level representations improve performance across a subset of selected semantic phenomena. |
| Approach: | They combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. |
| Outcome: | The proposed model improves performance on a subset of selected semantic phenomena. |
Global Reasoning over Database Structures for Text-to-SQL Parsing (D19-1)
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| Challenge: | Existing semantic parsers only select a set of database constants at training time . current models only consider local information, not global ones . |
| Approach: | They propose a semantic parser that globally reasons about the structure of the query to make a more contextually-informed selection of database constants. |
| Outcome: | The proposed model increases accuracy from 39.4% to 47.4% on a zero-shot semantic parsing dataset with complex databases. |
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese (2021.acl-short)
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| Challenge: | Neural semantic parsers have obtained acceptable results in parsing DRSs . previous studies have focused on parse of DRS in English, but have focused only on a few languages . |
| Approach: | They propose to use character sequences as input to map meaning representations to string format. |
| Outcome: | The proposed models learn the meaning of a series of semantic phenomena by taking sentences as input and outputting the corresponding DRSs, without the aid of any extra linguistic information. |
Context Dependent Semantic Parsing: A Survey (2020.coling-main)
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| Challenge: | Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. |
| Approach: | They propose to use contextual information to translate natural language utterances into machine-readable meaning representations. |
| Outcome: | The proposed methods do not utilize contextual information, which could boost the semantic parsing systems. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
| Approach: | They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. |
| Outcome: | The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set. |
Discourse Representation Structure Parsing (P18-1)
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| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |
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. |
A Span Selection Model for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |