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Found 8 papers, 4 papers with code
Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions
In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said.
Learning to Recognize Dialect Features
Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples.
Analyzing the Framing of 2020 Presidential Candidates in the News
In this study, we apply NLP methods to learn about the framing of the 2020 Democratic Presidential candidates in news media.
The Role of Verb Semantics in Hungarian Verb-Object Order
We test the effect of lexical semantics on the ordering of verbs and their objects by grouping verbs into 11 semantic classes.
P\'art\'elet: A Hungarian Corpus of Propaganda Texts from the Hungarian Socialist Era
P{\'a}rt{\'e}let was the official journal of the governing party during the Hungarian socialism from 1956 to 1989, hence it represents the direct political agitation and propaganda of the dictatorial system in question.
GoEmotions: A Dataset of Fine-Grained Emotions
Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior.
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Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force.
Transforming Question Answering Datasets Into Natural Language Inference Datasets
Existing datasets for natural language inference (NLI) have propelled research on language understanding.
