Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm
Authors: Rui Yan, Dongyan Zhao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From the experimental results, we verify the effectiveness of the new neural generative conversation paradigm. We conduct extensive experiments in a variety of humancomputer conversation setups and evaluate the performance with automatic evaluation metrics and human judgments. The experimental results are positive. |
| Researcher Affiliation | Academia | Rui Yan1,2, Dongyan Zhao1,2 1 Institute of Computer Science and Technology (ICST), Peking University 2 Beijing Institute of Big Data Research {ruiyan, zhaody}@pku.edu.cn |
| Pseudocode | No | The paper describes the Deep Dual Fusion Model using mathematical equations and prose but does not include a distinct pseudocode block or an algorithm label. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | We use the data which contain a large number of human conversations from [Yao et al., 2017; Tao et al., 2018]. The data are crawled from open Web, where the users publish messages visible to the public, and then receive a bunch of subsequent replies to their utterances. We conducted the same data filtering and cleaning used in [Yan et al., 2016a]. |
| Dataset Splits | Yes | We applied the validation set. All of the parameters were chosen and tuned empirically. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'standard Chinese word segmentation' and 'stochastic gradient descent' but does not specify any software libraries or tools with version numbers required for reproduction. |
| Experiment Setup | Yes | We use 512-dimensional word embeddings, and they were initialized randomly. The cell units have 300 hidden units for each dimension. We used stochastic gradient descent (with a mini-batch size of 100) for optimization, gradient computed by standard back propagation. Initial learning rate was set to 0.8, and a multiplicative learning rate decay was applied. |