1 code implementation • EMNLP 2021 • Anuradha Welivita, Yubo Xie, Pearl Pu
Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset.
no code implementations • 25 Oct 2022 • Junze Li, Mengjie Zhao, Yubo Xie, Antonis Maronikolakis, Pearl Pu, Hinrich Schütze
Humor is a magnetic component in everyday human interactions and communications.
no code implementations • 22 May 2022 • Yubo Xie, Junze Li, Pearl Pu
The knowledge captured in this graph bears potential for conversational systems to understand how people offer acknowledgement, consoling, and a wide range of empathetic responses in social conversations.
no code implementations • 10 Aug 2021 • Yubo Xie, Pearl Pu
In this paper, we give an overview of commonsense reasoning in natural language processing, which requires a deeper understanding of the contexts and usually involves inference over implicit external knowledge.
no code implementations • SEMEVAL 2021 • Yubo Xie, Junze Li, Pearl Pu
The task aims at predicting whether the given text is humorous, the average humor rating given by the annotators, and whether the humor rating is controversial.
1 code implementation • CoNLL (EMNLP) 2021 • Yubo Xie, Pearl Pu
Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help.
no code implementations • 25 Dec 2020 • Anuradha Welivita, Yubo Xie, Pearl Pu
We propose a novel large-scale emotional dialogue dataset, consisting of 1M dialogues retrieved from the OpenSubtitles corpus and annotated with 32 emotions and 9 empathetic response intents using a BERT-based fine-grained dialogue emotion classifier.
no code implementations • ACL 2021 • Yubo Xie, Junze Li, Pearl Pu
Humor recognition has been widely studied as a text classification problem using data-driven approaches.
1 code implementation • 15 Aug 2019 • Yubo Xie, Ekaterina Svikhnushina, Pearl Pu
Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing.