Search Results for author: Alisa Liu

Found 9 papers, 7 papers with code

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

1 code implementation16 Jan 2022 Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi

Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns.

Natural Language Inference Text Generation

Generated Knowledge Prompting for Commonsense Reasoning

1 code implementation ACL 2022 Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi

It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models.

Language Modelling

Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation

1 code implementation23 Jun 2020 Alisa Liu, Alexander Fang, Gaëtan Hadjeres, Prem Seetharaman, Bryan Pardo

In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain.

Music Generation

Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach

1 code implementation23 Jun 2020 Alexander Fang, Alisa Liu, Prem Seetharaman, Bryan Pardo

Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems.

Model selection for deep audio source separation via clustering analysis

no code implementations23 Oct 2019 Alisa Liu, Prem Seetharaman, Bryan Pardo

We compare our confidence-based ensemble approach to using individual models with no selection, to an oracle that always selects the best model and to a random model selector.

Audio Source Separation Model Selection

CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense

1 code implementation WS 2019 Michael Chen, Mike D{'}Arcy, Alisa Liu, Fern, Jared ez, Doug Downey

To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.

Common Sense Reasoning Question Answering +1

CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense

1 code implementation8 Apr 2019 Michael Chen, Mike D'Arcy, Alisa Liu, Jared Fernandez, Doug Downey

To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.

 Ranked #1 on Common Sense Reasoning on CODAH (using extra training data)

Common Sense Reasoning Question Answering +1

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