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Improving Compositional Generalization in Semantic Parsing
[article]
2020
arXiv
pre-print
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In this work, we investigate compositional generalization in semantic parsing, a natural test-bed for compositional generalization, as output programs are constructed from sub-components. ...
We analyze a wide variety of models and propose multiple extensions to the attention module of the semantic parser, aiming to improve compositional generalization. ...
In this paper, we thoroughly analyze the impact of different modeling choices on compositional generalization in 5 semantic parsing datasets-four that are text-to-SQL datasets, and DROP, a dataset for ...
arXiv:2010.05647v1
fatcat:mj7nlhfbizgbdbmwk4yrbezd4m
Improving Compositional Generalization in Semantic Parsing
2020
Findings of the Association for Computational Linguistics: EMNLP 2020
unpublished
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In this work, we investigate compositional generalization in semantic parsing, a natural test-bed for compositional generalization, as output programs are constructed from sub-components. ...
We analyze a wide variety of models and propose multiple extensions to the attention module of the semantic parser, aiming to improve compositional generalization. ...
In this paper, we thoroughly analyze the impact of different modeling choices on compositional generalization in 5 semantic parsing datasets-four that are text-to-SQL datasets, and DROP, a dataset for ...
doi:10.18653/v1/2020.findings-emnlp.225
fatcat:ncfdgbci5vhszbj2tn2bdn2njm
SUBS: Subtree Substitution for Compositional Semantic Parsing
[article]
2022
arXiv
pre-print
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Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. ...
However, prior work only leveraged superficial grammar or rules for data augmentation, which resulted in limited improvement. ...
This work is funded in part by a grant from Amazon and Salesforce. ...
arXiv:2205.01538v1
fatcat:2yikyid4yzg23ffjhkgpneen7u
Deep Image Harmonization
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. ...
In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization ...
This work is supported in part by the NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. Portions of this work were performed while Y.-H. Tsai was an intern at Adobe Research. ...
doi:10.1109/cvpr.2017.299
dblp:conf/cvpr/TsaiSLSL017
fatcat:gd2ww7pfdnferdowtqiez6jx3m
Fish Transporters and Miracle Homes: How Compositional Distributional Semantics can Help NP Parsing
2013
Conference on Empirical Methods in Natural Language Processing
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In this work, we argue that measures that have been shown to quantify the degree of semantic plausibility of phrases, as obtained from their compositionally-derived distributional semantic representations ...
We show that our plausibility cues outperform a strong baseline and significantly improve performance when used in combination with state-of-the-art features. ...
Besides paving the way to a more general integration of compositional distributional semantics in syntactic parsing, the proposed methodology provides a new way to evaluate composition functions. ...
dblp:conf/emnlp/LazaridouVB13
fatcat:aiwc77plnjhipmmj4kretsxllq
Deep Image Harmonization
[article]
2017
arXiv
pre-print
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Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. ...
In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization ...
In order to further improve harmonization results, it is natural to consider semantics of the composite foreground region. ...
arXiv:1703.00069v1
fatcat:72z2nc6ttbb65k4t6y3l4ufihy
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
[article]
2021
arXiv
pre-print
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In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. ...
Our novel architecture achieves up to an 81% reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets. ...
., 2020) , an extension of the compositional form proposed in for task oriented semantic parsing. ...
arXiv:2104.04923v1
fatcat:cmpiibt3v5co5fjxf7vhssuvvm
Iterative Utterance Segmentation for Neural Semantic Parsing
[article]
2020
arXiv
pre-print
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Experiments on Geo, ComplexWebQuestions, and Formulas show that our framework can consistently improve performances of neural semantic parsers in different domains. ...
On data splits that require compositional generalization, our framework brings significant accuracy gains: Geo 63.1 to 81.2, Formulas 59.7 to 72.7, ComplexWebQuestions 27.1 to 56.3. ...
Therefore, the lack of compositional generalization ability is still a challenging problem in neural semantic parsing (Finegan-Dollak et al. 2018; Keysers et al. 2020 ). ...
arXiv:2012.07019v1
fatcat:sciq2qg7unaqffgylcpu7drvmu
Compositional Generalization via Semantic Tagging
[article]
2021
arXiv
pre-print
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Experimental results on three semantic parsing datasets show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms. ...
Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions ...
Conclusions We presented a two-stage decoding framework, aiming to improve compositional generalization in neural semantic parsing. ...
arXiv:2010.11818v2
fatcat:ymqwzq2qfbeuri2aycjhce2kvi
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder
[article]
2022
arXiv
pre-print
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We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. ...
At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination. ...
Finally in Section 2.3 we describe the generation procedure of semantic parse tree given the input utterance. ...
arXiv:2109.04500v2
fatcat:tdre55dztzh3bgvbudqxlyerze
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-Based Encoder
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. ...
At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination. ...
Validity of Generated Trees In this section we compare the validity of the semantic parse trees generated by our and baseline approaches. ...
doi:10.1609/aaai.v36i10.21355
fatcat:sfh7kylegrgoflncyuey3zebzi
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
[article]
2022
arXiv
pre-print
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Meanwhile, recent work has shown considerable improvements on many NLP tasks from model scaling. Can scaling up model size also improve compositional generalization in semantic parsing? ...
We observe that fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional generalization in semantic parsing evaluations. ...
compositional generalization in semantic parsing? ...
arXiv:2205.12253v1
fatcat:wpffg53mlfce5hp5dt3ta4ri4m
Empirically-motivated Generalizations of CCG Semantic Parsing Learning Algorithms
2014
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
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Learning algorithms for semantic parsing have improved drastically over the past decade, as steady improvements on benchmark datasets have shown. ...
In this paper we investigate whether they can generalize to a novel biomedical dataset that differs in important respects from the traditional geography and air travel benchmark datasets. ...
In an effort to investigate and improve the generalization capacity of existing learning algorithms for semantic parsing, we develop a novel, natural experimental setting, and we test whether current semantic ...
doi:10.3115/v1/e14-1037
dblp:conf/eacl/GlassY14
fatcat:aczjodkqa5axtagccnbbvhniq4
Compositional Generalization in Dependency Parsing
[article]
2022
arXiv
pre-print
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To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). ...
Dependency parsing, however, lacks a compositional generalization benchmark. ...
To address the issues with the CFQ semantic parsing benchmark, we studied compositional generalization in syntactic parsing. ...
arXiv:2110.06843v2
fatcat:yhlen62dmva67jahhwbnpoyucu
GLR parsing with multiple grammars for natural language queries
2002
ACM Transactions on Asian Language Information Processing
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Parser composition imparts a robust parsing capability in our framework, and hence obtains a higher understanding performance when compared to using a single GLR parser. ...
A parser composition technique then combines the parsers' outputs to produce an overall parse that is the same as the output parse of single parser. ...
By comparison, our semantic interpreter only generates the key-value pair "time=tonight" in our frame, and the PI is not available to us for incorporation in our generated semantic frames. ...
doi:10.1145/568954.568956
fatcat:4f3as7wqqreetpceow7fjnhdni
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