Conversational Word Embedding for Retrieval-Based Dialog System
This work addresses the challenge of enhancing retrieval-based dialog systems for applications like chatbots, but it appears incremental as it builds on existing embedding methods with a novel adaptation.
The paper tackles the problem of learning word embeddings for retrieval-based dialog systems by proposing PR-Embedding, which uses conversation pairs to capture information like knowledge and common sense, resulting in improved response selection quality in experiments.
Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs $ \left\langle{post, reply} \right\rangle$ to learn word embedding. Different from previous works, PR-Embedding uses the vectors from two different semantic spaces to represent the words in post and reply. To catch the information among the pair, we first introduce the word alignment model from statistical machine translation to generate the cross-sentence window, then train the embedding on word-level and sentence-level. We evaluate the method on single-turn and multi-turn response selection tasks for retrieval-based dialog systems. The experiment results show that PR-Embedding can improve the quality of the selected response. PR-Embedding source code is available at https://github.com/wtma/PR-Embedding