Search Results for author: Xinliang Frederick Zhang

Found 7 papers, 5 papers with code

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis

no code implementations4 Nov 2022 Changyuan Qiu, Winston Wu, Xinliang Frederick Zhang, Lu Wang

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.

Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

1 code implementation2 Nov 2022 Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang

We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them.

Stance Detection

Towards More Robust Natural Language Understanding

no code implementations1 Dec 2021 Xinliang Frederick Zhang

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 Understanding Pretrained Language Models

Identifying inherent disagreement in natural language inference

1 code implementation NAACL 2021 Xinliang Frederick Zhang, Marie-Catherine de Marneffe

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.

Natural Language Inference

CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

2 code implementations30 Oct 2020 Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao, Simon Lin, Huan Sun

Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts.

Domain Adaptation Question Answering +2

COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

1 code implementation EMNLP 2021 Xinliang Frederick Zhang, Heming Sun, Xiang Yue, Simon Lin, Huan Sun

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.


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