1 code implementation • CoNLL (EMNLP) 2021 • Kevin Stowe, Nils Beck, Iryna Gurevych
Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models.
1 code implementation • ACL 2022 • Kevin Stowe, Prasetya Utama, Iryna Gurevych
Natural language inference (NLI) has been widely used as a task to train and evaluate models for language understanding.
1 code implementation • IWCS (ACL) 2021 • Kevin Stowe, Jenette Preciado, Kathryn Conger, Susan Windisch Brown, Ghazaleh Kazeminejad, James Gung, Martha Palmer
The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses.
no code implementations • 23 Apr 2024 • Kevin Stowe, Benny Longwill, Alyssa Francis, Tatsuya Aoyama, Debanjan Ghosh, Swapna Somasundaran
Natural language generation tools are powerful and effective for generating content.
1 code implementation • 25 Apr 2023 • Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe, Gözde Gül Şahin, Nafise Sadat Moosavi, Luke Bates, Dominic Petrak, Richard Eckart de Castilho, Iryna Gurevych
Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP.
1 code implementation • 28 Nov 2022 • Kevin Stowe, Debanjan Ghosh, Mengxuan Zhao
This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items.
1 code implementation • ACL 2021 • Kevin Stowe, Tuhin Chakrabarty, Nanyun Peng, Smaranda Muresan, Iryna Gurevych
Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions.
1 code implementation • 17 Apr 2021 • Aniket Pramanick, Tilman Beck, Kevin Stowe, Iryna Gurevych
Language use changes over time, and this impacts the effectiveness of NLP systems.
no code implementations • 23 Oct 2020 • Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe, Iryna Gurevych, Stefan Kramer
We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.
no code implementations • 28 Feb 2020 • Kevin Stowe, Leonardo Ribeiro, Iryna Gurevych
This work describes the task of metaphoric paraphrase generation, in which we are given a literal sentence and are charged with generating a metaphoric paraphrase.
no code implementations • CONLL 2019 • Kevin Stowe, Sarah Moeller, Laura Michaelis, Martha Palmer
In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks.
no code implementations • COLING 2018 • Kevin Stowe, Martha Palmer, Jennings Anderson, Marina Kogan, Leysia Palen, Kenneth M. Anderson, Rebecca Morss, Julie Demuth, Heather Lazrus
As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions.
no code implementations • WS 2018 • Kevin Stowe, Jennings Anderson, Martha Palmer, Leysia Palen, Ken Anderson
We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.
no code implementations • WS 2018 • Kevin Stowe, Martha Palmer
Identification of metaphoric language in text is critical for generating effective semantic representations for natural language understanding.