AliMe’s Stratified Framework

The majority of intelligent matching processes in use today fall into three main categories- rule-based matching, retrieval, and DL. The technology behind AliMe is based on a combination of all three.

The dialogue system is thus divided into the following strata:

1. Intention identification stratum

This stratum identifies the underlying intention for each message, classifying them and then extracting their attributes. Since intentions determine the subsequent domain identification flow, the intention stratum is a necessary first step in initiating contextual and domain data model processes.

The technical framework for AliMe’s intention and matching stratification

2. Answering stratum:

Questions are matched and identified to generate answers; AliMe’s dialogue system employs three answering strategies according to different intentions:

a. FAQs such as “What should I do if I’ve forgotten my password?” trigger a query on knowledge graph or retrieval model.

The knowledge graph is constructed by mining entities and phrases, the relations of which are predefined, from the vast pool of data available. Though knowledge graph-based methods accurately identify answers, they also accrue higher maintenance costs and looser initial data structures AliMe’s Q&A design overcomes this by integrating traditional retrieval models.

Mining data for creating knowledge graphs

b. Tasks such as “I’d like to book a one-way flight from New York to Paris for tomorrow” can be solved by the intention commitment + slot filing matching or deep reinforcement learning (DRL) model.

c. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL).

The chitchat domain mainly involves two kinds of models- the retrieval-based model and the deep generative model. The former makes selections from a fixed corpus of answers relevant to a given query, while the latter is more advanced, generating answers without relying on any corpus. The integrated merits of the two models form the core of AliMe’s chat engine. First, the candidate data sets are brought up using the traditional retrieval model; then, candidate sets are re-ranked through the Seq2Seq model; the top answer candidate is chosen when the ranking score is higher than the preset threshold, failing which the seq2seq model is activated to generate an answer.