Search Results for author: Matthew Richardson

Found 9 papers, 4 papers with code

KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

2 code implementations ACL 2021 Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson

The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains.

Question Answering SQL Parsing +2

NL-EDIT: Correcting semantic parse errors through natural language interaction

1 code implementation NAACL 2021 Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah

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.

Semantic Parsing Text-To-SQL

Structure-Grounded Pretraining for Text-to-SQL

no code implementations NAACL 2021 Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, Matthew Richardson

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.


RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

4 code implementations ACL 2020 Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, Matthew Richardson

The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.

Relation Semantic Parsing +1

DyNet: The Dynamic Neural Network Toolkit

4 code implementations15 Jan 2017 Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin

In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.

graph construction

Blending LSTMs into CNNs

no code implementations19 Nov 2015 Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Ozlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton

We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

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