Retrieval Reply Dialogue Method and System Combining Keywords and Semantic Understanding Representation

Abstract

The invention discloses a search-type reply dialogue method and system combining keywords and semantic understanding representations. The system combines vector representations of two levels of granularity, which are respectively bag-of-words vector representations and semantic understanding representations. Keyword information and context-based semantic understanding are also considered, which greatly improves the performance of the retrieval reply model. In the present invention, the Chinese pre-training model Bert network model is adopted to obtain the sentence vector representation, which not only understands the sentence meaning, but also eliminates the error caused by the word vector weighting. The system uses the Bert network model to train the classification task on its own single-round dialogue—the task of whether the dialogue matches or not. Through fine-tuning, it learns the weight of the linear layer and activation function in Bert. The system uses the fine-ranking model LGMRanker, which can directly predict the relative order of replies related to the query and return a sorted list.

Description

technical field [0001] The invention relates to the field of artificial intelligence retrieval reply dialogue, in particular to a retrieval reply dialogue method and system combining keywords and semantic understanding representations. Background technique [0002] At present, the dialogue system has attracted more and more attention in various fields. It is mainly a system that allows machines to understand and process human language through dialogue. Dialogue processes that can be modeled. Dialogue modeling is not a simple task, it is a comprehensive entity involving technologies in multiple directions such as understanding, generation, and interaction. The complexity of its dialogue scenarios, such as customer service, voice assistants, chatting, etc., also creates the complexity of the dialogue system. [0003] Retrieval dialogue is a classic solution that abstracts a dialogue problem into a search problem. Early dialogue systems were implemented using this solution. U...

AI Technical Summary

Problems solved by technology

In the current technology, the RNN-based model is often used to obtain the semantic representation of the dialogue text. When the text is too long, the key information is often not captured, and the redundant information cannot be filtered out. The quality of the relevant replies retrieved not tall

However, simply using keyword representations to retrieve relevant matching replies does not achieve a smooth and natural reply effect in terms of semantics.

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