 We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing. Such data can be used to train automated dialogue agents performing customer care tasks for the enterprises or organizations. In particular, the framework takes the documents as input and generates the tasks for obtaining the annotations for simulating dialog flows. The dialog flows are used to guide the collection of utterances produced by crowd workers. The outcomes include dialogue data grounded in the given documents, as well as various types of annotations that help ensure the quality of the data and the flexibility to (re)composite dialogues. There has been growing interest in using automated dialogue agents to assist customers through online chat. However, despite recent effort in training automated agents with human-human dialogues, it often faces the bottleneck that a large number of chat logs or simulated dialogues with various scenarios are required. Meanwhile, enterprises and organizations often own a large number of business documents that could address customers' requests, such as technical documentation, policy guidance and Q&A webpages. However, customers would still prefer having interactive conversations with agents instead of searching and reading through lengthy documents. Taken together, a promising solution is to build machine assisted agents that could perform task-oriented dialogues that are based on the content of the business documents. In task-oriented dialogues for customer care, a recurrent theme is a diagnostic process -identifying the contextual conditions that apply to the customer to retrieve the most relevant solutions. Meanwhile, business documents often contain similar information, with prior conditions, in for example if-clauses or subtitles, followed by corresponding solutions. Therefore, these documents can be used to guide diagnostic dialogues-we call them document-grounded dialogues. For example, the sample business document in Figure 1 contains information for an agent to perform the dialogue on the right, where P-n (S-n) denotes text span n labeled a precondition (solution) and "O-D" denotes "out of domain". The preconditions are expressed in various ways such as subtitles or if-clauses, followed by corresponding solution if that precondition applies. In this work, we hypothesize that an essential capability for a dialogue agent to perform goal-oriented information retrieval tasks should be to recognize the preconditions and their associated solutions covered by the given documents and then use them to carry out the diagnostic interactions. Towards this goal, we introduce DOC2DIAL, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing. It aims to minimize the effort for handcrafting dialog flows that is specific to the document but still introduce dynamic dialog scenes. It also provides quality control over the data collection process. We guide our investigation with the following principles: 1) We aim to identify the document content that provides solution(s) to a user's request as well as describes the prerequisites required. 2) The generated dialog flows should be specific to the given document without relying on heavily supervised or handcrafted work. 3) The generated data tasks should be easy to scale -feasible to crowdsourcing platforms and could be updated with respect to changes in the document. Thus, we propose a pipeline of three interconnected tasks for dialogue composition based on business documents: (1) labeling text spans as preconditions or solutions in a given documents (TextAnno); (2) identifying the relation(s) between these preconditions or solutions (RelAnno; (3) simulating dialog flows based on the linked preconditions/solutions and applying them to guide the collection of human generated utterances(DialAnno). For the dialogue collection, we could deploy it via both synchronized and asynchronized processes. An asychronized process allows crowd workers to work on the production and evaluation of individual turns without the constraints of timing or having a dialog partner. The outcome includes the document grounded dialogues as well as various types of implicit and explicit annotations that help ensure the quality of the data. Such data sets can be used to develop various types of dialogue agent technologies in the context of customer care. Our primary contributions can be summarized as follows: (1) we introduce DOC2DIAL, an end-to-end framework 1 to generate task-oriented conversational data from business documents; (2) we propose a novel pipeline approach of three interconnected tasks that minimizes the dialog flow crafting manual effort and enables comprehensive cross-task validation to ensure the quality of dialogue data; (3) the system supports both synchronized and asynchronized dialogue collection processes. We demonstrate that such setting allows flexible dialogue composition by utterances, which are guided by the given document content and its annotations.
We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing for train automated dialogue agents
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