Papers by Tomek Strzalkowski
BeSt: The Belief and Sentiment Corpus (2022.lrec-1)
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Jennifer Tracey, Owen Rambow, Claire Cardie, Adam Dalton, Hoa Trang Dang, Mona Diab, Bonnie Dorr, Louise Guthrie, Magdalena Markowska, Smaranda Muresan, Vinodkumar Prabhakaran, Samira Shaikh, Tomek Strzalkowski
| Challenge: | a corpus of propositional content is a set of cognitive attitudes of different agents towards a text . propositional attitudes are a cognitive attitude, including belief and sentiment, towards . |
| Approach: | They propose a corpus which records cognitive state: who believes what, who has what sentiment . they use newswire and discussion forums in Chinese, English, and Spanish . |
| Outcome: | The proposed corpus records who believes what (i.e., factuality) and who has what sentiment towards what. |
Gaining and Losing Influence in Online Conversation (L18-1)
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| Challenge: | a study aimed to determine if people who are influential in online discussions retain influence when placed in a topic that is less familiar or perhaps not as interesting. |
| Approach: | They conducted a study to determine if people who are highly influential retain influence when moving to a topic that is less familiar or perhaps not as interesting. |
| Outcome: | The results show that people who are highly influential in group discussions lose influence when placed in a topic that is less familiar or perhaps not as interesting. |
Social Convos: Capturing Agendas and Emotions on Social Media (2024.lrec-main)
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| Challenge: | Social media traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. |
| Approach: | They propose a method to extract influence indicators from messages circulating among groups . they build upon the concept of a convo to identify influential authors . |
| Outcome: | The proposed approach extracts influence indicators from messages circulating among groups of users discussing particular topics. |
Learning to Plan and Realize Separately for Open-Ended Dialogue Systems (2020.findings-emnlp)
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Sashank Santhanam, Zhuo Cheng, Brodie Mather, Bonnie Dorr, Archna Bhatia, Bryanna Hebenstreit, Alan Zemel, Adam Dalton, Tomek Strzalkowski, Samira Shaikh
| Challenge: | Existing approaches to natural language generation are construed as end-to-end systems . however, some issues persist, such as coherence of output and repetition/hallucination of tokens . |
| Approach: | They propose to decouple natural language generation into two phases: planning and realization. |
| Outcome: | The proposed approach performs better than an end-to-end approach. |
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)
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| Challenge: | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios. |
| Approach: | They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses. |
| Outcome: | The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses. |
Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media (2024.lrec-main)
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| Challenge: | a social media analysis of online influence campaigns can reveal the sources of agenda setting . annotated data is limited or nonexistent, but there are methods to detect agenda control . |
| Approach: | They propose a method for detecting instances of agenda control through social media . they use a modest corpus of tweets centered on the 2022 french presidential election . |
| Outcome: | The proposed method overcomes the requirement for large annotated training dataset. |
Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling (2024.findings-acl)
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| Challenge: | Existing models that detect multiple FL features in text are not effective in authorship attribution tasks. |
| Approach: | They propose a multi-task Figurative Language Model that learns to detect multiple FL features in text at once. |
| Outcome: | The proposed model outperforms specialized binary models in AA tasks or outperformed binary models on three datasets. |