Florin Brad


2020

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Neural Approaches for Natural Language Interfaces to Databases: A Survey
Radu Cristian Alexandru Iacob | Florin Brad | Elena-Simona Apostol | Ciprian-Octavian Truică | Ionel Alexandru Hosu | Traian Rebedea
Proceedings of the 28th International Conference on Computational Linguistics

A natural language interface to databases (NLIDB) enables users without technical expertise to easily access information from relational databases. Interest in NLIDBs has resurged in the past years due to the availability of large datasets and improvements to neural sequence-to-sequence models. In this survey we focus on the key design decisions behind current state of the art neural approaches, which we group into encoder and decoder improvements. We highlight the three most important directions, namely linking question tokens to database schema elements (schema linking), better architectures for encoding the textual query taking into account the schema (schema encoding), and improved generation of structured queries using autoregressive neural models (grammar-based decoders). To foster future research, we also present an overview of the most important NLIDB datasets, together with a comparison of the top performing neural models and a short insight into recent non deep learning solutions.

2018

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Natural Language Interface for Databases Using a Dual-Encoder Model
Ionel Alexandru Hosu | Radu Cristian Alexandru Iacob | Florin Brad | Stefan Ruseti | Traian Rebedea
Proceedings of the 27th International Conference on Computational Linguistics

We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user’s request in natural language. The sketch is obtained by using placeholders for specific entities in the SQL query, such as column names, table names, aliases and variables, in a process similar to semantic parsing. The first step is to apply a sequence-to-sequence (SEQ2SEQ) model to determine the most probable SQL sketch based on the request in natural language. Then, a second network designed as a dual-encoder SEQ2SEQ model using both the text query and the previously obtained sketch is employed to generate the final SQL query. Our approach shows improvements over previous approaches on two recent large datasets (WikiSQL and SENLIDB) suitable for data-driven solutions for natural language interfaces for databases.

2017

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Dataset for a Neural Natural Language Interface for Databases (NNLIDB)
Florin Brad | Radu Cristian Alexandru Iacob | Ionel Alexandru Hosu | Traian Rebedea
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability. Moreover, the lack of a large corpus to be used as a standard benchmark has made data-driven approaches difficult to develop and compare. In this paper, we revisit the problem of NLIDBs and recast it as a sequence translation problem. To this end, we introduce a large dataset extracted from the Stack Exchange Data Explorer website, which can be used for training neural natural language interfaces for databases. We also report encouraging baseline results on a smaller manually annotated test corpus, obtained using an attention-based sequence-to-sequence neural network.

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Neural Paraphrase Generation using Transfer Learning
Florin Brad | Traian Rebedea
Proceedings of the 10th International Conference on Natural Language Generation

Progress in statistical paraphrase generation has been hindered for a long time by the lack of large monolingual parallel corpora. In this paper, we adapt the neural machine translation approach to paraphrase generation and perform transfer learning from the closely related task of entailment generation. We evaluate the model on the Microsoft Research Paraphrase (MSRP) corpus and show that the model is able to generate sentences that capture part of the original meaning, but fails to pick up on important words or to show large lexical variation.