Hamidreza Shahidi


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TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
Peng Xu | Wenjie Zi | Hamidreza Shahidi | Ákos Kádár | Keyi Tang | Wei Yang | Jawad Ateeq | Harsh Barot | Meidan Alon | Yanshuai Cao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough accuracy in the cross-database setting to allow good usability in practice. This work presents TURING, a NLDB system toward bridging this gap. The cross-domain semantic parser of TURING with our novel value prediction method achieves 75.1% execution accuracy, and 78.3% top-5 beam execution accuracy on the Spider validation set (Yu et al., 2018b). To benefit from the higher beam accuracy, we design an interactive system where the SQL hypotheses in the beam are explained step-by-step in natural language, with their differences highlighted. The user can then compare and judge the hypotheses to select which one reflects their intention if any. The English explanations of SQL queries in TURING are produced by our high-precision natural language generation system based on synchronous grammars.


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Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data
Hamidreza Shahidi | Ming Li | Jimmy Lin
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text generation from structured and unstructured data. We consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively. Table-to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models. Experimental results demonstrate that a basic attention-based seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks. Code is available at https://github.com/h-shahidi/2birds-gen.