End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT -2 (2020)

Abstract: The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to integrate with external systems or to provide interpretable information about why the system generated a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the human evaluation, our dialogue system achieved the success rate of 68.32%, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).

## introduction the goal-oriented dialogue system helps users achieve their goals such as requesting information or executing commands via natural language conversations. it is thus crucial for the dialogue system to keep track of the dialogue flow and carry out an effective conversation, even when the user goal is complicated or the dialogue flow is suddenly changed. ## end-to-end neural pipeline for goal-oriented dialogue system we now describe our end-to-end neural pipeline for the goal-oriented dialogue system based on gpt-2. our system consists of (1) the gpt-2 model finetuned on the delexicalized version of multiwoz dataset (section 3.2) and (2) the database query module. we take the pre-trained gpt-2 model and fine-tune it to follow the steps of the dialogue management pipeline. figure 2 illustrates an overall architecture with a concrete example. the overview of the process followed by our model is as follows: ## related work transfertransfo @xcite was the first attempt to incorporate a large-scale pre-trained language model into a chit-chat dialogue system. using gpt as a backbone, their fine-tuning approach ranked first in the automatic evaluation and second in the human evaluation in the convai2 competition @xcite . our model is mainly inspired by this work, extending to goal-oriented dialogues using gpt-2. parallel and independent to our work towards dstc8 submission, @xcite also demonstrated a neural model for goaloriented dialogue systems by fine-tuning gpt-2 on the multiwoz dataset. however, they only handle dialogue-context-to-text task, which outputs the system response given the dialogue history, the ground-truth dialogue state, and the database. in our case, no oracle information related to database ## conclusion in this paper, we presented an end-to-end monolithic neural model for goal-oriented dialogues that learns to follow the core steps in the dialogue management pipeline. since our model outputs all the intermediate results in the dialogue management pipeline, it is easy to integrate with external systems and to interpret why the system generates a particular response. the experimental results from human evaluation show evidence that our approach can provide very natural human-level interaction for goal-oriented dialogues, advancing the stateof-the-art in conversational ai agents. this also demonstrates the power of large-scale pre-trained language models to be adopted for building end-toend goal-oriented dialogue systems.