Christopher Meek

Also published as: Chris Meek


2021

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Structure-Grounded Pretraining for Text-to-SQL
Xiang Deng | Ahmed Hassan Awadallah | Christopher Meek | Oleksandr Polozov | Huan Sun | Matthew Richardson
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel pretraining tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERTLARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. All the code and data used in this work will be open-sourced to facilitate future research.

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NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction
Ahmed Elgohary | Christopher Meek | Matthew Richardson | Adam Fourney | Gonzalo Ramos | Ahmed Hassan Awadallah
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.

2020

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Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context
Shashank Srivastava | Oleksandr Polozov | Nebojsa Jojic | Christopher Meek
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration. Our approach investigates a new direction for semantic parsing that models explaining a demonstration in a context, rather than mapping explanations to demonstrations. By leveraging the idea of inverse semantics from program synthesis to reason backwards from observed demonstrations, we ensure that all considered interpretations are consistent with executable actions in any context, thus simplifying the problem of search over logical forms. We present a dataset of explanations paired with demonstrations for web-based tasks. Our methods show better task completion rates than a supervised semantic parsing baseline (40% relative improvement on average), and are competitive with simple exploration-and-demonstration based methods, while requiring no exploration of the environment. In learning to align explanations with demonstrations, basic properties of natural language syntax emerge as learned behavior. This is an interesting example of pragmatic language acquisition without any linguistic annotation.

2016

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The Value of Semantic Parse Labeling for Knowledge Base Question Answering
Wen-tau Yih | Matthew Richardson | Chris Meek | Ming-Wei Chang | Jina Suh
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yi Yang | Wen-tau Yih | Christopher Meek
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Semantic Parsing for Single-Relation Question Answering
Wen-tau Yih | Xiaodong He | Christopher Meek
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
Kai-Wei Chang | Wen-tau Yih | Bishan Yang | Christopher Meek
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Question Answering Using Enhanced Lexical Semantic Models
Wen-tau Yih | Ming-Wei Chang | Christopher Meek | Andrzej Pastusiak
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Multi-Relational Latent Semantic Analysis
Kai-Wei Chang | Wen-tau Yih | Christopher Meek
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Combining Heterogeneous Models for Measuring Relational Similarity
Alisa Zhila | Wen-tau Yih | Christopher Meek | Geoffrey Zweig | Tomas Mikolov
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Computational Approaches to Sentence Completion
Geoffrey Zweig | John C. Platt | Christopher Meek | Christopher J.C. Burges | Ainur Yessenalina | Qiang Liu
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

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Learning Discriminative Projections for Text Similarity Measures
Wen-tau Yih | Kristina Toutanova | John C. Platt | Christopher Meek
Proceedings of the Fifteenth Conference on Computational Natural Language Learning