Papers with deep neural network architecture
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 . |
Content-based Dwell Time Engagement Prediction Model for News Articles (N19-2)
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| Challenge: | Existing studies on article dwell time prediction are noisy and may not show the actual user engagement or satisfaction. |
| Approach: | They propose a deep neural network architecture to extract emotion, event and entity features from an article and learn interactions among them. |
| Outcome: | The proposed model outperforms state-of-the-art models on a real newspaper dataset. |
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller (D18-1)
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| Challenge: | Existing approaches to machine reading comprehension are limited in understanding, up to a few paragraphs, failing to comprehend lengthy documents. |
| Approach: | They propose a deep neural network architecture to handle a long-range dependency in RC tasks. |
| Outcome: | The proposed method outperforms existing methods especially for lengthy documents. |
Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels (2020.coling-main)
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| Challenge: | Neural networks can capture expressive language features, but insights into the link between words and sentences are difficult to acquire automatically. |
| Approach: | They propose a deep neural network architecture that explicitly wires lower and higher linguistic components and evaluate its ability to perform the same task at different hierarchical levels. |
| Outcome: | The proposed model outperforms equivalent models that are not incentivized towards compositional representations. |
Thread Popularity Prediction and Tracking with a Permutation-invariant Model (D18-1)
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| Challenge: | a task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. |
| Approach: | They propose a deep neural network architecture to model the expected cumulative reward of a recommendation (action) they employ a greedy procedure to approximate the action that maximizes the predicted Q-value . |
| Outcome: | The proposed approach outperforms the state-of-the-art on five real-world datasets. |
Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
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| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
| Approach: | They propose a domain adaptation layer that learns weights to combine a generic and a specific word embedding into a DA embeddable. |
| Outcome: | The proposed approach improves on binary and multi-class classification tasks using popular encoder architectures. |