Papers by Roma Patel
“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models (2021.emnlp-main)
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| Challenge: | Several studies have identified such linguistic classes of words that occur frequently in natural language text and are bias-inducing by virtue of their framing effects. |
| Approach: | They propose to use linguistic cues to induce subtle biases through implied sentiment and presupposed facts to influence the distribution of the generated text. |
| Outcome: | The proposed models are sensitive to these framing effects, but show that they lead to measurable style and topic differences in the generated text, leading to language that is, on average, more polarised and more skewed towards controversial entities and events. |
Recognizing Multimodal Entailment (2021.acl-tutorials)
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Cesar Ilharco, Afsaneh Shirazi, Arjun Gopalan, Arsha Nagrani, Blaz Bratanic, Chris Bregler, Christina Funk, Felipe Ferreira, Gabriel Barcik, Gabriel Ilharco, Georg Osang, Jannis Bulian, Jared Frank, Lucas Smaira, Qin Cao, Ricardo Marino, Roma Patel, Thomas Leung, Vaiva Imbrasaite
| Challenge: | This tutorial introduces the multimodal entailment task for detecting semantic alignments . the task requires fine-grained understanding of visual and linguistic semantics questions . |
| Approach: | This tutorial introduces the multimodal entailment task to machine learning . it introduces a dataset for recognizing multimodal alignments . |
| Outcome: | This tutorial introduces the multimodal entailment task . it can be useful for detecting semantic alignments when a single modality alone is not enough . |
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index (2021.naacl-main)
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| Challenge: | Using the full vocabulary results in less explainable and memory intensive models. |
| Approach: | They propose a vocabulary selection method that views words as members of a team trying to maximize the model's performance. |
| Outcome: | The proposed method outperforms baseline models on multiple tasks and datasets. |
Value Profiles for Encoding Human Variation (2025.emnlp-main)
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Taylor Sorensen, Pushkar Mishra, Roma Patel, Michael Henry Tessler, Michiel A. Bakker, Georgina Evans, Iason Gabriel, Noah Goodman, Verena Rieser
| Challenge: | Using value profiles and a steerable decoder model to estimate ratings is crucial for personalization, pluralistic model alignment, and computational social science. |
| Approach: | They propose to represent individuals using value profiles and a steerable decoder model to estimate ratings conditioned on a value profile or other rater information. |
| Outcome: | The proposed model interpretably changes ratings according to semantic profile differences and is well-calibrated. |
Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches (2023.findings-emnlp)
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| Challenge: | People rely heavily on context to enrich meaning beyond what is literally said. |
| Approach: | They analyze how task goals, environmental contexts, and communicative affordances in each work enrich linguistic meaning. |
| Outcome: | The proposed frameworks are based on linguistic goals, environmental contexts, and communicative affordances to enrich linguistic meaning. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (P18-1)
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| Challenge: | In 2015 alone, about 100 manuscripts describing randomized controlled trials for medical interventions were published every day. |
| Approach: | They propose a corpus of 5,000 medical articles annotated with demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured. |
| Outcome: | The proposed corpus includes 5,000 medical articles describing clinical randomized controlled trials. |
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding (2020.emnlp-main)
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| Challenge: | Room-Across-Room (RxR) is a vision-and-language navigation dataset that addresses gaps in existing ones by addressing known biases in paths and eliciting more references to visible entities. |
| Approach: | They introduce a new Vision-and-Language Navigation (VLN) dataset that addresses biases in paths and elicits more references to visible entities. |
| Outcome: | The proposed model learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. |
Syntactic Patterns Improve Information Extraction for Medical Search (N18-2)
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| Challenge: | Medical professionals search the literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s). |
| Approach: | They propose to exploit the availability of structured abstracts to extract medically relevant information from syntactic patterns. |
| Outcome: | The proposed models differ from the constituent unigrams in the extracted patterns, suggesting that they capture contextual information that is otherwise lost. |