Papers by Diana Inkpen
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (D19-62)
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| Challenge: | Existing methods for mental illness detection have limited data available for training . lack of sufficient annotated data and inability to extract explanations on the derived outcome have restricted researchers to use traditional methods. |
| Approach: | They propose to use emotional patterns identified by clinical practitioners to enhance the prediction capabilities of a mental illness detection model built using a deep neural network architecture. |
| Outcome: | The proposed method achieves a task-specific AUC higher than 0.90 . it compares multi-task learning with multi-channel convolutional neural network and multiple inputs to methods such as multi-class classification . |
A Context-Aware Contrastive Learning Framework for Hateful Meme Detection and Segmentation (2025.findings-naacl)
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| Challenge: | Empirical experiments show HateSieve surpasses existing LMMs in performance with fewer trainable parameters . |
| Approach: | They propose a framework to enhance detection and segmentation of hateful elements in memes by creating a triplet dataset and an Image-Text Alignment module. |
| Outcome: | HateSieve features a new framework that creates semantically correlated memes and generates contextual embeddings for accurate meme segmentation. |
Authorship Identification for Literary Book Recommendations (C18-1)
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| Challenge: | Existing systems that predict whether a user likes a book are collaborative filtering and content-based recommendation. |
| Approach: | They propose a system that learns authors' style and uses it to recommend books . they also evaluated whether similar books/authors were annotated similarly by experts . |
| Outcome: | The proposed system gives better accuracy when compared with state-of-the-art methods. |
Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
| Approach: | They propose a topic modeling approach that uses document contextual token embeddings to find topics and find topic spans within documents. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics. |
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose to use a large stance detection dataset in the political domain to detect stances on twitter. |
| Outcome: | The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection. |
Neural Natural Language Inference Models Enhanced with External Knowledge (P18-1)
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| Challenge: | Existing datasets that allow for complex models to be trained are limited . if data is not available, can machines learn all knowledge needed to perform natural language inference? |
| Approach: | They propose to enrich neural natural language inference models with external knowledge . they propose to use this knowledge to build NLI models to leverage it . |
| Outcome: | The proposed models improve on the SNLI and MultiNLI datasets. |