Adaboost with Auto-Evaluation for Conversational Models
Authors: Juncen Li, Ping Luo, Ganbin Zhou, Fen Lin, Cheng Niu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we do some empirical experiments to evaluate our method. We demonstrate that Aw E visibly boosts the performance of single model and also outperforms the other ensemble methods for conversational models. |
| Researcher Affiliation | Collaboration | Juncen Li1, Ping Luo2,3, Ganbin Zhou2,3, Fen Lin1, Cheng Niu1 1 We Chat Search Application Department, Tencent, China 2 Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 3 University of Chinese Academy of Sciences, Beijing 100049, China |
| Pseudocode | Yes | Algorithm 1 AUTO-EVALUATION ADABOOST |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | We collect nearly 14 million post-response pairs from Tencent Weibo . Removing spams and advertisements from that dataset, there are only 803,716 high-quality post-response pairs retained. |
| Dataset Splits | Yes | Table 1: Training pairs 773,315 Validation pairs 28,949 Test posts 1000 |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only discusses the model architecture and training settings. |
| Software Dependencies | No | The paper mentions using RNN Encoder-Decoder with GRU and specific settings like beam size, but does not specify any software names with version numbers (e.g., Python, TensorFlow, PyTorch versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We use 1-layer GRU with 512 cells for both the encoder and the decoder. Both embedding dimensions are set to 128. We initialize all parameters with the uniform distribution between -0.1 and 0.1. And We set the minibatch size to 256. We use beam search method to do the generation and we set beam size to 10. |