Papers by Samira Shaikh
A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event (2021.acl-srw)
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| Challenge: | construal level theory (CLT) uses concreteness as covariate to analyze language around political import events. |
| Approach: | They propose to include psycholinguistic measures of concreteness as covariates in topic models to analyze the language around an event of political import. |
| Outcome: | The proposed model incorporates measures of concreteness as covariates to inform the analysis of language around the 2017 rally. |
Persona-aware Multi-party Conversation Response Generation (2024.lrec-main)
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| Challenge: | Recent advances in natural language generation have addressed multi-turn dialogues . interactions with more than 2 participants pose new and interesting challenges for MPC modeling . |
| Approach: | They propose to include persona attributes of speaker and addressee relevant to each utterance in a multi-party conversation dataset and a persona-aware heterogeneous graph transformer response generation model. |
| Outcome: | The proposed model includes persona attributes of speaker and addressee relevant to each utterance. |
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. |
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. |
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models (D19-50)
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| Challenge: | Detecting fake news is not well established yet, but it can be classified under several labels: false, biased, or framed to mislead the readers. |
| Approach: | They propose a deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda using a corpus from a challenge. |
| Outcome: | The proposed model outperforms the baseline model on a dataset from the challenge NLP4IF 2019 . |