Papers with neural network framework
Neural Multitask Learning for Simile Recognition (D18-1)
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| Challenge: | Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. |
| Approach: | They propose a neural network framework for simile sentence classification, simile component extraction and language modeling. |
| Outcome: | The proposed framework outperforms rule-based and feature-based approaches in simile sentence classification and simile component extraction tasks. |
Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)
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| Challenge: | Existing methods to measure scholarly impact of documents without citations only consider word frequency change. |
| Approach: | They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts. |
| Outcome: | The proposed model outperforms existing models on document influence evaluation without citations. |
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction (2023.emnlp-main)
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| Challenge: | Emotion recognition is a crucial task for human conversation understanding . multimodal data, e.g., language, voice, and facial expressions, add complexity to the task. |
| Approach: | They propose a relational temporal Graph Neural Network with Auxiliary Cross-Modality Interaction framework that captures conversation-level cross-modality interactions and utterance-level temporal dependencies with modality-specific manner for conversation understanding. |
| Outcome: | The proposed framework captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding. |
Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models (2026.acl-long)
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| Challenge: | Despite their distinct external representations, a deeper analysis reveals their intrinsic nature: instructions serve as a natural language compression devised by humans for data governing specific mapping patterns, whereas parameters act as 'neuro compression' of the same task data. |
| Approach: | They propose a neural network framework to model and learn the bi-directional mappings between instructions and parameters of large language models by evaluating it on the tasks of instruction deduction and induction. |
| Outcome: | The proposed framework can map one of the instructions/parameters to the other by evaluating it on the tasks of instruction deduction and induction. |