PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output.
Stephen H. Bach +25 more
semanticscholar +1 more source
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
M. Lewis +7 more
semanticscholar +1 more source
Making Sense of Language Signals for Monitoring Radicalization
Applied Sciences, 2022 Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include ...
Óscar Araque +7 more
doaj +1 more source
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding [PDF]
BlackboxNLP@EMNLP, 2018 Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data.
Alex Wang +5 more
semanticscholar +1 more source
A large annotated corpus for learning natural language inference [PDF]
Conference on Empirical Methods in Natural Language Processing, 2015 Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research
Samuel R. Bowman +3 more
semanticscholar +1 more source
CLAP Learning Audio Concepts from Natural Language Supervision
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Mainstream machine listening models are trained to learn audio concepts under the paradigm of one class label to many recordings focusing on one task.
Benjamin Elizalde +3 more
semanticscholar +1 more source
Text2Motion: from natural language instructions to feasible plans [PDF]
Autonomous Robots, 2023 We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that
Kevin Lin +4 more
semanticscholar +1 more source
Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets
Politics and Governance, 2020 This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora.
Sebastian Haunss +6 more
doaj +1 more source
Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for ...
Myra Cheng, Esin Durmus, Dan Jurafsky
semanticscholar +1 more source
PIQA: Reasoning about Physical Commonsense in Natural Language [PDF]
AAAI Conference on Artificial Intelligence, 2019 To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems.
Yonatan Bisk +4 more
semanticscholar +1 more source