Search Results for author: Ruosong Yang

Found 9 papers, 3 papers with code

Long Text and Multi-Table Summarization: Dataset and Method

1 code implementation8 Feb 2023 Shuaiqi Liu, Jiannong Cao, Ruosong Yang, Zhiyuan Wen

Within a report document, the salient information can be scattered in the textual and non-textual content.

Document Summarization Informativeness +1

Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation

1 code implementation3 Apr 2024 Zhiyuan Wen, Jiannong Cao, Yu Yang, Ruosong Yang, Shuaiqi Liu

To utilize affectivity within dialog content for accurate personality recognition, we fine-tuned a pre-trained language model specifically for emotion recognition in conversations, facilitating real-time affective annotations for utterances.

Emotion Recognition Language Modelling +2

Decode with Template: Content Preserving Sentiment Transfer

no code implementations LREC 2020 Zhiyuan Wen, Jiannong Cao, Ruosong Yang, Senzhang Wang

The two major challenges in existing works lie in (1) effectively disentangling the original sentiment from input sentences; and (2) preserving the semantic content while transferring the sentiment.

GGP: Glossary Guided Post-processing for Word Embedding Learning

no code implementations LREC 2020 Ruosong Yang, Jiannong Cao, Zhiyuan Wen

To enhance corpus based word embedding models, researchers utilize domain knowledge to learn more distinguishable representations via joint optimization and post-processing based models.

Low-Resource Court Judgment Summarization for Common Law Systems

no code implementations7 Mar 2024 Shuaiqi Liu, Jiannong Cao, Yicong Li, Ruosong Yang, Zhiyuan Wen

Current summarization datasets are insufficient to satisfy the demands of summarizing precedents across multiple jurisdictions, especially when labeled data are scarce for many jurisdictions.

Data Augmentation

Personality-affected Emotion Generation in Dialog Systems

no code implementations3 Apr 2024 Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun

Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition.

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