Papers by Shirley Anugrah Hayati
StyLEx: Explaining Style Using Human Lexical Annotations (2023.eacl-main)
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| Challenge: | Large pre-trained language models often learn spurious domain-specific words to make predictions. |
| Approach: | They propose a model that learns from human annotated explanations of stylistic features and jointly predicts them as model explanations. |
| Outcome: | The proposed model can provide human like stylistic lexical explanations without sacrificing performance on in-domain and out-of-domain datasets. |
Retrieval-Based Neural Code Generation (D18-1)
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Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig
| Challenge: | Existing methods to generate program source code from natural language are not able to generate complex code due to a lack of ability to memorize large and complex structures. |
| Approach: | They propose a method that uses subtree retrieval to explicitly reference existing code examples within a neural code generation model. |
| Outcome: | The proposed method improves performance on two code generation tasks by up to +2.6 BLEU. |
INSPIRED: Toward Sociable Recommendation Dialog Systems (2020.emnlp-main)
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| Challenge: | Existing studies on recommendation dialog systems lack a study on communication strategies used by human speakers for making successful and persuasive recommendations. |
| Approach: | They propose to annotate a dataset of human-human movie recommendation dialogs with sociable recommendation strategies. |
| Outcome: | The proposed model outperforms the baseline model in automatic and human evaluation. |
Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games (2023.findings-acl)
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Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James Rehg, Diyi Yang
| Challenge: | Existing studies on persuasive behavior modeling focus on textual dialogues . a multimodal dataset is available for persuasion modeling . |
| Approach: | They propose a multimodal dataset for modeling persuasive behaviors using visual signals. |
| Outcome: | The proposed dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting and 26,647 utterance level annotations of persuasion strategy and game level annotation of deduction game outcomes. |
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
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| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |
Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica (2021.emnlp-main)
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| Challenge: | Using pre-trained models, people use different styles to express their interpersonal goal and attitude in their communication. |
| Approach: | They use a dataset to collect lexicon usages across styles using two lenses: human perception and machine word importance. |
| Outcome: | The proposed model can predict human perception and machine word importance based on a popular style classifier like BERT . human- and machine-identified words share significant overlap for some styles . |