TaskBot
TaskBot是一个基于深度学习、机器学习、规则系统的任务驱动型多轮对话框架，欢迎star、contribute。100行代码，实现你的chatbot。
特性
- 多轮跨场景语义继承
- 模块化组件，面向快速的2次定制化开发任务
- 多套替代方案，系统冷启动及热更新。
Models
Preprocessing
- Query Expandsion| dictionary based, similarity based, context based
- Spelling error correction
- segment
Netual Language Representation
- Bag of Words:|tfidf
- Network|word2vec[4], glove[3], EMlo[2]
- Topic Model| LSI, LDA, LSA[5]
Netural Language Understanding
- Named entity recognization| BiRNN-CRF[6,7], Fuzzy&Keyword Match
- Entity Normalization|Rule, Keyword Match
- Intent Classification| FastText[8, 9], TextCNN, Multi-Round-LSTM, Pattern Match
- Emotion recognization
Dialog Management
- Dialog state tracker
- Action Policy| High confidence, Rule, Reinforcement Learning
- record
Recovery Generation
- Retrieval based| Text similarity rank, Metric Learning, rule based, Keyword Match
- Knowlage Graph based[9, 10, 11]|
- Generation Model| seq2seq
Reference
[1] github.com/deepmipt/DeepPavlov
[2] EMol:Deep contextualized word embeddings
[3] GloVe: Global Vectors for Word Representation
[4] Word2vec: Distributed Representations of Words and Phrases and their Compositionality
[5] Topic Model: LDA, LSI, LSA
[6] BiLSTM-CRF: Empower Sequence Labeling with Task-Aware Neural Language Model
[7] A BiLSTM-CRF inplement with PyTorch
[8] FastText: Compressing text classification models
[9] Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs.
[10] Socher, R., Chen, D., Manning, C. D., & Ng, A. (2013). Reasoning with neural tensor networks for knowledge base completion. In Advances in Neural Information Processing Systems (pp. 926-934).
[11] Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems (pp. 2787-2795).
LICENSE
TaskBot is licensed under the GNU GENERAL PUBLIC LICENSE VERSION 3.0