Matt Gardner

Universal Adversarial Triggers for Attacking and Analyzing NLP

2019

Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Tailor: Generating and Perturbing Text with Semantic Controls

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, which is expensive and hard to generalize. We present Tailor, a semantically-controlled …

Coverage-based Example Selection for In-Context Learning

2023 · arXiv (Cornell University)

In-context learning (ICL), the ability of large language models to perform novel tasks by conditioning on a prompt with a few task examples, requires these examples to be informative about the test instance. The standard …

Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

2017

We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations:

Simple and Effective Multi-Paragraph Reading Comprehension

2018

We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively …

Deep Contextualized Word Representations

2018

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume …

AllenNLP: A Deep Semantic Natural Language Processing Platform

2018

Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, Luke Zettlemoyer. Proceedings of Workshop for NLP Open Source Software (NLP-OSS). 2018.

Linguistic Knowledge and Transferability of Contextual Representations

2019

Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, Noah A. Smith. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long …

Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling

2019

Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often have difficulty …

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

2019

Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In …

Compositional Questions Do Not Necessitate Multi-hop Reasoning

2019

Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions …

QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships

2019

Many natural la guage questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but …

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

2019

Oyvind Tafjord, Matt Gardner, Kevin Lin, Peter Clark. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.