Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
نویسندگان
چکیده
In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multiple answer entities or no entity at all. Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-ofvocabulary entities. We propose a fully data-driven generative dialogue system GenDS that is capable of generating responses based on input message and related knowledge base (KB). To generate arbitrary number of answer entities even when these entities never appear in the training set, we design a dynamic knowledge enquirer which selects different answer entities at different positions in a single response, according to different local context. It does not rely on the representations of entities, enabling our model deal with out-ofvocabulary entities. We collect a human-human conversation data (ConversMusic) with knowledge annotations. The proposed method is evaluated on CoversMusic and a public question answering dataset. Our proposed GenDS system outperforms baseline methods significantly in terms of the BLEU, entity accuracy, entity recall and human evaluation. Moreover,the experiments also demonstrate that GenDS works better even on small datasets.
منابع مشابه
Key-Value Retrieval Networks for Task-Oriented Dialogue
Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. The model is end-to-end differentiable and does not need to explicitly model dialogue state or b...
متن کاملEnd-to-end optimization of goal-driven and visually grounded dialogue systems
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision may f...
متن کاملSemantic and dialogic annotation for automated multilingual customer service
One central goal of the AMITIÉS multilingual humancomputer dialogue project is to create a dialogue management system capable of engaging the user in human-like conversation in a specific domain. To that end, we have developed new methods for the manual annotation of spoken dialogue transcriptions from European financial call centers. We have modified the DAMSL dialogic schema to create a dialo...
متن کاملAugmenting End-to-End Dialog Systems with Commonsense Knowledge
Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impa...
متن کاملFlexible And Personalizable Mixed-Initiative Dialogue Systems
This paper describes our vision for a future time when end users of mixed-initiative spoken dialogue systems will be able to dynamically configure the system to suit their personalized goals. We argue that spoken dialogue systems will only become a common utility in society once they can be reconfigured, essentially instantaneously, to support a new working vocabulary within a new domain or sub...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1709.04264 شماره
صفحات -
تاریخ انتشار 2017