Search Results for author: Chan Young Park

Found 13 papers, 5 papers with code

Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation

no code implementations WS 2019 Chan Young Park, Yulia Tsvetkov

In this paper, we introduce a phrase-based NMT model built upon continuous-output NMT, in which the decoder generates embeddings of words or phrases.

Machine Translation NMT +1

Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

2 code implementations EACL 2021 Jimin Sun, Hwijeen Ahn, Chan Young Park, Yulia Tsvetkov, David R. Mortensen

Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages.

Cross-Lingual Transfer Dependency Parsing +2

Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia

no code implementations21 Oct 2020 Chan Young Park, Xinru Yan, Anjalie Field, Yulia Tsvetkov

Specific lexical choices in narrative text reflect both the writer's attitudes towards people in the narrative and influence the audience's reactions.

Controlled Analyses of Social Biases in Wikipedia Bios

1 code implementation31 Dec 2020 Anjalie Field, Chan Young Park, Kevin Z. Lin, Yulia Tsvetkov

In this work, we present a methodology for analyzing Wikipedia pages about people that isolates dimensions of interest (e. g., gender), from other attributes (e. g., occupation).

Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

no code implementations24 May 2022 Chan Young Park, Julia Mendelsohn, Anjalie Field, Yulia Tsvetkov

NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation.

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

1 code implementation15 May 2023 Shangbin Feng, Chan Young Park, YuHan Liu, Yulia Tsvetkov

We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks.

Fairness Misinformation

Analyzing Norm Violations in Live-Stream Chat

no code implementations18 May 2023 Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Young Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park

Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter.

TalkUp: Paving the Way for Understanding Empowering Language

no code implementations23 May 2023 Lucille Njoo, Chan Young Park, Octavia Stappart, Marvin Thielk, Yi Chu, Yulia Tsvetkov

Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare.

Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions

no code implementations13 Nov 2023 Sachin Kumar, Chan Young Park, Yulia Tsvetkov

GEN-Z is generative, as it measures the LM likelihood of input text, conditioned on natural language descriptions of labels.

Language Modelling text-classification +3

P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models

no code implementations16 Nov 2023 YuHan Liu, Shangbin Feng, Xiaochuang Han, Vidhisha Balachandran, Chan Young Park, Sachin Kumar, Yulia Tsvetkov

In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article.

News Summarization

CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge

no code implementations10 Apr 2024 Yu Ying Chiu, Liwei Jiang, Maria Antoniak, Chan Young Park, Shuyue Stella Li, Mehar Bhatia, Sahithya Ravi, Yulia Tsvetkov, Vered Shwartz, Yejin Choi

Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions, that modern LLMs fail at, in a gamified manner.

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