Question Generation from SQL Queries Improves Neural Semantic Parsing

Guo, Daya; Sun, Yibo; Tang, Duyu; Duan, Nan; Yin, Jian; Chi, Huisheng; Cao, James; Chen, Peng; Zhou, Ming

doi:10.18653/v1/d18-1188

Cited by 45 publications

(40 citation statements)

References 42 publications

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“…We perform a PSEUDO baseline following the setup in Sennrich et al (2016) and Guo et al (2018). The pre-trained LF 2Q or Q2LF model is used to generate pseudo query, logical f orm pairs from unlabeled logical forms or unlabeled queries, which extends the training set.…”

Section: Results and Analysismentioning

confidence: 99%

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Semantic Parsing with Dual Learning

Cao¹,

Zhu²,

Liu³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on OVERNIGHT dataset.

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Section: Results and Analysismentioning

confidence: 99%

“…Su and Yan (2017) use multiple source domains to reduce the cost of collecting data for the target domain. Guo et al (2018) pre-train a question generation model to produce pseudo-labeled data as a supplement.…”

Section: Related Workmentioning

confidence: 99%

Semantic Parsing with Dual Learning

Cao¹,

Zhu²,

Liu³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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“…Such models were first introduced for machine translation, that is, for mapping a sentence in one natural language to the corresponding sentences in another (Bahdanau, Cho, & Bengio, 2015; Luong, Pham, & Manning, 2015). With some changes, this neural architecture has been extensively adapted for semantic parsing (Dong & Lapata, 2016; Guo et al, 2018; Jia & Liang, 2016; Sun et al, 2018; X. Xu, Liu, & Song, 2017; Zhong, Xiong, & Socher, 2017) and more specifically for the KGQA task (Cheng & Lapata, 2018; Guo, Tang, Duan, Zhou, & Yin, 2018; He & Golub, 2016; C. Liang et al, 2017; Yin, Zhou, He, & Neubig, 2018).…”

Section: Neural Network‐based Kgqa Systemsmentioning

confidence: 99%

“…Many semantic parsing approaches rely on a decoder that generates production rules or more generally, actions, rather than logical form tokens (Cheng & Lapata, 2018; Guo, Sun, et al, 2018; Guo, Tang, et al, 2018; Lin, Bogin, Neumann, Berant, & Gardner, 2019; Rabinovich, Stern, & Klein, 2017; Shen et al, 2019; Yin & Neubig, 2017; Yin & Neubig, 2018; T. Yu et al, 2018). For example, suppose we would like to decode a query argmax (Astronauts, height).…”

Section: Neural Network‐based Kgqa Systemsmentioning

confidence: 99%

Introduction to neural network‐based question answering over knowledge graphs

Chakraborty

Lukovnikov²,

Maheshwari

et al. 2021

WIREs Data Min & Knowl

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Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network‐based systems. In this article, we provide an overview of these neural network‐based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Technologies > Artificial Intelligence

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“…Ke et al (2018) train models to ask questions in open-domain conversational systems to better interact with people. Guo et al (2018) develop a sequence-to-sequence model to generate natural language questions.…”

Section: Question Generationmentioning

confidence: 99%

Asking Clarification Questions in Knowledge-Based Question Answering

Xu¹,

Wang²,

Tang³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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The ability to ask clarification questions is essential for knowledge-based question answering (KBQA) systems, especially for handling ambiguous phenomena. Despite its importance, clarification has not been well explored in current KBQA systems. Further progress requires supervised resources for training and evaluation, and powerful models for clarification-related text understanding and generation. In this paper, we construct a new clarification dataset, CLAQUA, with nearly 40K open-domain examples. The dataset supports three serial tasks: given a question, identify whether clarification is needed; if yes, generate a clarification question; then predict answers base on external user feedback. We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation. Experiments show that the proposed model achieves better performance than strong baselines. The further analysis demonstrates that our dataset brings new challenges and there still remain several unsolved problems, like reasonable automatic evaluation metrics for clarification question generation and powerful models for handling entity sparsity. 1 * The work was done while Jingjing Xu and Yuechen Wang were interns in Microsoft Research, Asia. 1 The dataset and code will be released at https:// github.com/msra-nlc/MSParS_V2.0 What are the languages used to create the source code of Midori? I mean the first one. When you say the source code language used in the program Midori, are you referring to web browser Midori or operating system Midori? C.

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Question Generation from SQL Queries Improves Neural Semantic Parsing

Cited by 45 publications

References 42 publications

Semantic Parsing with Dual Learning

Semantic Parsing with Dual Learning

Introduction to neural network‐based question answering over knowledge graphs

Asking Clarification Questions in Knowledge-Based Question Answering

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