Skip to search formSkip to main contentSkip to account menu

Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

This paper presents an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time.
View on ACL (opens in a new tab)

Global Reasoning over Database Structures for Text-to-SQL Parsing

This work uses message-passing through a graph neural network to softly select a subset of database constants for the output query, conditioned on the question, and trains a model to rank queries based on the global alignment ofdatabase constants to question words.
View on ACL (opens in a new tab)

Evaluating Models’ Local Decision Boundaries via Contrast Sets

A more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data, and recommends that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets.
View on ACL (opens in a new tab)

Towards an argumentative content search engine using weak supervision

This work uses a weak signal to define weak signals for training DNNs to obtain significantly greater performance, and adapts the system to solve a recent argument mining task of identifying argumentative sentences in Web texts retrieved from heterogeneous sources, and obtain F1 scores comparable to the supervised baseline.
View on ACL (opens in a new tab)

Emergence of Communication in an Interactive World with Consistent Speakers

A new model and training algorithm is proposed, that utilizes the structure of a learned representation space to produce more consistent speakers at the initial phases of training, which stabilizes learning and increases context-independence compared to policy gradient and other competitive baselines.
View PDF on arXiv (opens in a new tab)

Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data

This work releases SEDE, a dataset with 12,023 pairs of utterances and SQL queries collected from real usage on the Stack Exchange website, and shows that these pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset.
View on ACL (opens in a new tab)

Evaluating NLP Models via Contrast Sets

A new annotation paradigm for NLP is proposed that helps to close systematic gaps in the test data, and it is recommended that after a dataset is constructed, the dataset authors manually perturb the test instances in small but meaningful ways that change the gold label, creating contrast sets.
View PDF on arXiv (opens in a new tab)

Language Generation with Recurrent Generative Adversarial Networks without Pre-training

It is shown that recurrent neural networks can be trained to generate text with GANs from scratch by slowly teaching the model to generate sequences of increasing and variable length, which vastly improves the quality of generated sequences compared to a convolutional baseline.
View PDF on arXiv (opens in a new tab)

Grammar-based Neural Text-to-SQL Generation

The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar. Grammar-based…
View PDF on arXiv (opens in a new tab)

COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images

This work proposes COVR, a new test-bed for visually-grounded compositional generalization with real images, and proposes an almost fully automatic procedure for generating question-answer pairs along with a set of context images.
View on ACL (opens in a new tab)
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)