Learning to Explain Ambiguous Headlines of Online News
Authors: Tianyu Liu, Wei Wei, Xiaojun Wan
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Utilizing automatic and manual evaluation metrics, we demonstrate the efficacy and the complementarity of the two methods, and the ambiguity-aware neural matching model achieves the state-of-the-art performance on this challenging task. |
| Researcher Affiliation | Academia | Tianyu Liu, Wei Wei, Xiaojun Wan Institute of Computer Science and Technology, Peking University The MOE Key Laboratory of Computational Linguistics, Peking University {ty liu, weiwei718, wanxiaojun}@pku.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions using 'Matchzoo Toolkit' with a GitHub link, but this is a third-party tool used by the authors, not the open-source code for their specific methodology. There is no explicit statement about releasing their own implementation's source code. |
| Open Datasets | Yes | We use the data set of [Wei and Wan, 2017], which contains 645 pieces of news with ambiguous headline. |
| Dataset Splits | Yes | The final data set contains 1500 pieces of news with ambiguous headline, with 1000 for training, 100 for validation and 400 for test. |
| Hardware Specification | No | No specific hardware (e.g., GPU model, CPU type, memory size, or cloud instance details) used for experiments is mentioned in the paper. |
| Software Dependencies | Yes | We use the ROUGE-1.5.5 toolkit to perform evaluation for the results. |
| Experiment Setup | Yes | For A2DRMM, we use 2-layer feed-forward network based on a histogram with the number of bins set to 60. For the gating network of A2DRMM, when using ambiguity-augmented embedding as input, we added a hidden layer of 5 nodes. All other parameters for training are set by default. |