In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content.
We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them.
Ideology is at the core of political science research.
Therefore, in order to have NLU models understand human language more effectively, it is expected to prioritize the study on robust natural language understanding.
Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text.
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts.
For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1, 236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.