Learning towards Selective Data Augmentation for Dialogue Generation
Authors: Xiuying Chen, Mingzhe Li, Jiayi Zhang, Xiaoqiang Xia, Chen Wei, Jianwei Cui, Xin Gao, Xiangliang Zhang, Rui Yan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on two publicly available datasets, i.e., Daily Dialog and Open Subtitles, show that our framework can improve the response generation performance with respect to various metrics. |
| Researcher Affiliation | Collaboration | Xiuying Chen1*, Mingzhe Li2*, Jiayi Zhang3, Xiaoqiang Xia3, Chen Wei3, Jianwei Cui3, Xin Gao1 , Xiangliang Zhang4, Rui Yan5 1Computational Bioscience Research Center, KAUST 2Ant Group 3 Xiaomi AI Lab 4 University of Notre Dame 5Gaoling School of Artificial Intelligence, Renmin University of China |
| Pseudocode | No | The paper describes its methodology using prose and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Following Cai et al. (2020), we conduct experiments on two English conversation datasets: (1) Daily Dialog (Li et al. 2017), a collection of real-world dialogues widely used in open-domain dialogue generation. ... (2) Open Subtitles (Lison and Tiedemann 2016), a group of human-human conversations converted from movie transcripts. |
| Dataset Splits | Yes | We split the Daily Dialog dataset to 54,889/6,005/5,700, and Open Subtitles to 64,000/8,000/8,000. |
| Hardware Specification | Yes | We implement our models in Tensor Flow on an NVIDIA GTX 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions implementing models in 'Tensor Flow' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | We truncate the input dialog to 20 words, the minimum decoding step is 10, and the maximum step is 30. The default σ in Equation 1 is set to 0.6 except in the augmentation percentage analysis. The batch size is set to 16, and we limit the vocabulary size to 50K. |