2019语言与智能技术竞赛-知识驱动对话B榜第5名源码和模型.zip

# knowledge-driven-dialogue-2019-lic 2019语言与智能技术竞赛[知识驱动对话](http://lic2019.ccf.org.cn/talk) B榜第5名方案<br> 由于线上部署对时间有要求，最终提交人工评估的版本删掉了一些全局主题特征，导致模型结果有所下降，最终人工评估第9名。A榜第四 B榜第五 ## Overview For building a proactive dialogue chatbot, we used a so-called generation-reranking method. First, the generative models(Multi-Seq2Seq) produce some candidate replies. Next, the re-ranking model is responsible for performing query-answer matching, to choice a reply as informative as possible over the produced candidates. A detailed paper to describle our solution is now avaliable at https://arxiv.org/pdf/1907.03590.pdf, please check. ### Data Augmentation We used four data augmentation techniques, Entity Generalization,Knowledge Selection,Switch,Conversation Extraction to construct multiple different dataset for training Seq2Seq models. One can use the scripts Seq2Seq/preclean_*.py to with slight modification of parameters to get 6 datasets. ### Seq2Seq Model For ensemble purpose we choose different encoders and decoders, i.e. LSTM cells and the Transformer. <br> #### Training - python preprocess.py - python train.py #### Testing python translate.py <br> All the config file of training & testing can be easily modified in the config/*.yml <br> In total, we trained 27 Seq2Seq model for ensemble. ### Answer rank We used a GBDT regressor for ranking. One may arugue that Why not use a neural network, such as BERT for ranking. Actually We tried, but it doesn't work well. #### Creating ranking dataset python create_gbdt_dataset.py #### Feature extraction python feature_util_multiprocess.py <br> The feature extractions partly reference the [Kaggle_HomeDepot](https://github.com/ChenglongChen/Kaggle_HomeDepot) by ChenglongChen ### Checkpoints It might take some extra time to upload the checkpoints because they are rather large in size.