G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents (2022)

Abstract: Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously. This paper summarizes our entries to agent response generation subtask in MultiDoc2Dial dataset. We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response. Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7% higher than the baseline model.

## introduction conversational question answering techniques have attracted widespread attention as a fusion of document-based question answering and dialogue generation techniques. most previous works have focused on single-document conversational question answering tasks, such as quac @xcite , coqa @xcite , doc2dial @xcite . as a more realistic task, goal-oriented dialogues generation is based on multiple documents as multidoc2dial @xcite faces challenges of identifying useful pieces of text from documents and generating response simultaneously. inspired by the method of open-domain question answering (openqa), the multidoc2dial task can be solved in a two-stage framework @xcite @xcite : (i) first to retrieve relevant passages from the knowledge source (the retriever) @xcite @xcite @xcite ; and (ii) second to produce an answer based on retrieved passages and the question (the generator) @xcite . what's more, the end-to-end methods which learn to retrieve and generate simultaneously @xcite also achieves good results. in multidoc2dial task, as the dialogue flow shifts among the grounding documents through the conversation which makes the current session relevant to multiple documents, identifying the content that best matches the question from the relevant documents becomes the biggest challenge. after analysis, it is found that two-stage methods fail to locate the answer span position from multiple relevant documents and generate irrelevant responses to the query. in this paper, we propose a groundingguided three-stage framework(retriever-reader-generator). the framework imitates the process of humans looking for answers from a browser: first read each relevant retrieved documents with question, and then combine the understanding of each document to generate a final answer. the generator of third stage can be guided by grounding spans, phrases in the retrieved document predicted by reader, mitigating the excessive deviation of the generated result from the correct response. since the same corpus data is shared among reader and generator, data distribution of the input for generator are different in training and inference. we also propose a data augmentation approach to alleviate the discrepancy between training and inference. to conclude, our contributions are as follows: ## method our proposed model g4 is a three-stage framework: (i) retrieve relevant passages from the knowledge(retriever). (ii) predict groundings based on retrieved passages(reader). (iii)generate a response based on the groundings predicted by reader and relevant passages from retriever(generator). ## conclusion in this paper, we propose a three-stage approach to the multidoc2dial task, which adds readers to a two-stage framework based on retriever and generator. we show that the grounding predicted by the reader can effectively mitigate the deviation of the generated result from the grounding and correct response. in future work, we plan to introduce grounding information in a more efficient way based on end-to-end models.