6 citations · 14 across the 20 of their papers we have counts for
8 papers · 1 filter
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
Ajay Patel, Jiacheng Zhu, Justin Qiu +4
Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets oft…
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution
Milad Alshomary, Narutatsu Ri, Marianna Apidianaki +3
Recent state-of-the-art authorship attribution methods learn authorship representations of texts in a latent, non-interpretable space, hindering their usability in real-world appli…
MASIVE: Open-Ended Affective State Identification in English and Spanish
Nicholas Deas, Elsbeth Turcan, Iván Pérez Mejía +1
In the field of emotion analysis, much NLP research focuses on identifying a limited number of discrete emotion categories, often applied across languages. These basic sets, howeve…
Solving Zebra Puzzles Using Constraint-Guided Multi-Agent Systems
Shmuel Berman, Kathleen McKeown, Baishakhi Ray
Prior research has enhanced the ability of Large Language Models (LLMs) to solve logic puzzles using techniques such as chain-of-thought prompting or introducing a symbolic represe…
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings
Zachary Horvitz, Ajay Patel, Kanishk Singh +3
The goal of text style transfer is to transform the style of texts while preserving their original meaning, often with only a few examples of the target style. Existing style trans…
Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models
Zachary Horvitz, Jingru Chen, Rahul Aditya +4
Humor is a fundamental facet of human cognition and interaction. Yet, despite recent advances in natural language processing, humor detection remains a challenging task that is com…
Social Orientation: A New Feature for Dialogue Analysis
Todd Morrill, Zhaoyuan Deng, Yanda Chen +3
There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm…
Parallel Structures in Pre-training Data Yield In-Context Learning
Yanda Chen, Chen Zhao, Zhou Yu +2
Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update. Howe…