5 citations · 12 across the 9 of their papers we have counts for
12 papers
Approximating 1-Wasserstein Distance with Trees
Makoto Yamada, Yuki Takezawa, Ryoma Sato +3
Wasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applicatio…
Fixed Support Tree-Sliced Wasserstein Barycenter
Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva +2
The Wasserstein barycenter has been widely studied in various fields, including natural language processing, and computer vision. However, it requires a high computational cost to…
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Markus Dreyer, Mengwen Liu, Feng Nan +2
Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documen…
Efficient Retrieval Optimized Multi-task Learning
Hengxin Fun, Sunil Gandhi, Sujith Ravi
Recently, there have been significant advances in neural methods for tackling knowledge-intensive tasks such as open domain question answering (QA). These advances are fueled by co…
Transductive Learning for Abstractive News Summarization
Arthur Bražinskas, Mengwen Liu, Ramesh Nallapati +2
Pre-trained and fine-tuned news summarizers are expected to generalize to news articles unseen in the fine-tuning (training) phase. However, these articles often contain specifics,…
GoEmotions: A Dataset of Fine-Grained Emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko +3
Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can b…