Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Authors: Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results from automatic and human evaluations demonstrate that our methods significantly boost informativeness and diversity. |
| Researcher Affiliation | Industry | Yizhe Zhang Michel Galley Jianfeng Gao Zhe Gan Xiujun Li Chris Brockett Bill Dolan Microsoft Research, Redmond, WA, USA {yizzhang,mgalley,jfgao,zhgan,xiul,chrisbkt,billdol}@microsoft.com |
| Pseudocode | No | The paper describes its methods in prose and with mathematical equations, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using 'Reddit' and 'Twitter' datasets but does not provide concrete access information (specific links, DOIs, or formal citations) for these datasets, nor does it explicitly state they are publicly available in the form used. |
| Dataset Splits | Yes | We randomly partition the data as (80%, 10%, 10%) to construct the training, validation and test sets. |
| Hardware Specification | Yes | All experiments are conducted using NVIDIA K80 GPUs. |
| Software Dependencies | No | The paper mentions general software components like 'CNN-LSTM framework' and 'word2vec embedding' but does not specify version numbers for any key software dependencies or libraries. |
| Experiment Setup | Yes | The filter size, stride and the word embedding dimension were set to 5, 2 and 300, respectively, following [46]. The hidden unit size of H0 was set to 100. We set λ to be 0.1 and the supervised-loss balancing parameter to be 0.001. We performed a beam search with width of 200 and choose the hyperparameter based on performance on the validation set. |