Papers with IPS
Fine-grained Conversational Decoding via Isotropic and Proximal Search (2023.emnlp-main)
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| Challenge: | Existing text decoding methods are not tailoring for dialogue generation. |
| Approach: | They propose a fine-grained conversational decoding method that generates a semantic-concentrated response while maintaining informativeness and discrimination against the context. |
| Outcome: | The proposed method outperforms existing decoding strategies in the dialogue field across both automatic and human evaluation metrics. |
IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)
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| Challenge: | Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details. |
| Approach: | They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning . |
| Outcome: | IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation. |
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies (P19-1)
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| Challenge: | Existing dependency parsing algorithms do not support directed acyclic graphs . a a systole-based dependency parses sentences using binary semantic relations that are not trees . |
| Approach: | They propose an iterative predicate selection algorithm for semantic dependency parsing . they train the algorithm using multi-task learning and task-specific policy gradient training . |
| Outcome: | The proposed model achieves a new state of the art on the SemEval 2015 task 18 dataset . |