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nlp:semantic_parsing

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nlp:semantic_parsing [2022/09/29 00:25] – [Domain Adaptation] jmflanignlp:semantic_parsing [2025/01/17 20:38] (current) – [Implicit Roles] jmflanig
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   * PropBank   * PropBank
     * [[https://arxiv.org/pdf/1804.08199.pdf|Strubell et al 2018 - Linguistically-Informed Self-Attention for Semantic Role Labeling]] (EMNLP 2018 Best Paper)     * [[https://arxiv.org/pdf/1804.08199.pdf|Strubell et al 2018 - Linguistically-Informed Self-Attention for Semantic Role Labeling]] (EMNLP 2018 Best Paper)
 +
 +==== Implicit Roles ====
 +Related: [[abstract_meaning_representation#Multi-sentence AMR]]. Implicit roles (also called implicit arguments or multi-sentence arguments) are semantic roles that exist across sentences.
 +
 +  * [[https://aclanthology.org/I17-1010.pdf|Ngoc Thi Do et al 2017 - Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments]]
 +  * [[https://arxiv.org/pdf/1911.03766.pdf|Ebner et al 2019 - Multi-Sentence Argument Linking]]
  
 ===== Supervised Parsing ===== ===== Supervised Parsing =====
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   * [[https://www.aclweb.org/anthology/N16-1052.pdf|Ambati et al 2016 - Shift-Reduce CCG Parsing using Neural Network Models]]   * [[https://www.aclweb.org/anthology/N16-1052.pdf|Ambati et al 2016 - Shift-Reduce CCG Parsing using Neural Network Models]]
   * [[https://arxiv.org/pdf/1607.01432.pdf|Lee et al 2016 - Global Neural CCG Parsing with Optimality Guarantees]]   * [[https://arxiv.org/pdf/1607.01432.pdf|Lee et al 2016 - Global Neural CCG Parsing with Optimality Guarantees]]
 +
 +==== QA Format ====
 +  * QASRL paper
 +  * QAMR paper
 +  * [[https://aclanthology.org/2021.emnlp-main.108.pdf|Pyatkin et al 2021 - Asking It All: Generating Contextualized Questions for any Semantic Role]]]]
  
 ===== Indirect Supervision ===== ===== Indirect Supervision =====
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   * [[https://arxiv.org/pdf/1704.08760.pdf|Iyar et al 2017 - Learning a Neural Semantic Parser from User Feedback]] Basic idea: parse to an executable representation, excecute it (try it on some examples), have humans look at the output and provide feedback (in few different ways)   * [[https://arxiv.org/pdf/1704.08760.pdf|Iyar et al 2017 - Learning a Neural Semantic Parser from User Feedback]] Basic idea: parse to an executable representation, excecute it (try it on some examples), have humans look at the output and provide feedback (in few different ways)
   * [[https://arxiv.org/pdf/2010.05190.pdf|Karamcheti et al 2020 - Learning Adaptive Language Interfaces through Decomposition]] (Percy Liang)   * [[https://arxiv.org/pdf/2010.05190.pdf|Karamcheti et al 2020 - Learning Adaptive Language Interfaces through Decomposition]] (Percy Liang)
 +
 +===== Prompting and Language Models =====
 +  * [[https://arxiv.org/pdf/2209.15003.pdf|Drozdov 2022 - Compositional Semantic Parsing with Large Language Models]]
  
 ===== Domain Adaptation ===== ===== Domain Adaptation =====
   * [[https://arxiv.org/pdf/2110.08458.pdf|Pasupat et al 2021 - Controllable Semantic Parsing via Retrieval Augmentation]] Retrieves similar exemplars, from training data, and uses essentially GPT-style few-shot learning method to produce the parse with a seq2seq model   * [[https://arxiv.org/pdf/2110.08458.pdf|Pasupat et al 2021 - Controllable Semantic Parsing via Retrieval Augmentation]] Retrieves similar exemplars, from training data, and uses essentially GPT-style few-shot learning method to produce the parse with a seq2seq model
 +
 +===== Data Augmentation =====
 +See also [[nlp:Data Augmentation]].
 +
 +  * [[https://arxiv.org/pdf/1606.03622.pdf|Jia & Liang 2016 - Data Recombination for Neural Semantic Parsing]]
  
 ===== Datasets ===== ===== Datasets =====
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   * [[Abstract Meaning Representation]]   * [[Abstract Meaning Representation]]
   * [[Language to Programs]]   * [[Language to Programs]]
 +  * [[Logic in NLP]]
   * [[Semantic Dependencies]]   * [[Semantic Dependencies]]
   * [[Semantic Representations]]   * [[Semantic Representations]]
  
nlp/semantic_parsing.1664411115.txt.gz · Last modified: 2023/06/15 07:36 (external edit)
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