activity
20142023
most citedModeling Compositionality with Multiplicative Recurrent Neural Networks

9 citations · 13 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2025

Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards

Jinyan Su, Claire Cardie

Large language models (LLMs) have demonstrated strong reasoning abilities in mathematical tasks, often enhanced through reinforcement learning (RL). However, RL-trained models freq…

cs.CL2023

Probing Representations for Document-level Event Extraction

Barry Wang, Xinya Du, Claire Cardie

The probing classifiers framework has been employed for interpreting deep neural network models for a variety of natural language processing (NLP) applications. Studies, however, h…

cs.CL2023

Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations

Wenting Zhao, Justin T. Chiu, Claire Cardie +1

Abductive reasoning aims to find plausible explanations for an event. This style of reasoning is critical for commonsense tasks where there are often multiple plausible explanation…

cs.CL2023

HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision

Wenting Zhao, Justin T. Chiu, Claire Cardie +1

Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers. This problem ha…

cs.CV2023

Fashionpedia-Ads: Do Your Favorite Advertisements Reveal Your Fashion Taste?

Mengyun Shi, Claire Cardie, Serge Belongie

Consumers are exposed to advertisements across many different domains on the internet, such as fashion, beauty, car, food, and others. On the other hand, fashion represents second…

cs.CV2023

Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste

Mengyun Shi, Serge Belongie, Claire Cardie

Existing fashion datasets do not consider the multi-facts that cause a consumer to like or dislike a fashion image. Even two consumers like a same fashion image, they could like th…