Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023)

Abstract: Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes. Machines are required to make a decision of “Yes/No/Inquire” or generate a follow-up question when the decision is “Inquire” based on retrieved rule texts, user scenario, user question and dialogue history. Recent studies try to reduce the information gap between decision-making and question generation, in order to improve the performance of generation. However, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages. To tackle the above problem, we propose a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decision-making and question generation in a global understanding manner. The extensive experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. Our model and code are publicly available at an anonymous link.

## introduction open-retrieval conversational machine reading comprehension (ocmrc) @xcite investigates real-life scenes, aiming to formulate multi-turn interactions between humans and machines in open-retrieval settings. as shown in figure 1 , given the user scenario and user question, machines are required to first retrieve related rule texts in the knowledge database, and then make a decision of yes/no/inquire or generate a follow-up question when the decision is figure 1 : an example in the ocmrc dataset. given the user scenario and user question, machines are required to first retrieve related rule texts in the knowledge database, and then make a decision of yes/no/inquire or generate a follow-up question when the decision is inquire based on retrieved rule texts, user scenario, user question and dialogue history. inquire based on retrieved rule texts, user scenario, user question and dialogue history. previous studies @xcite @xcite @xcite @xcite typically adopt pipeline frameworks based on pretrained language models (prlm) @xcite @xcite , as shown in figure 4 , these frameworks usually consist of three stages, includthus, the entailment reasoning utilized in decision-making is hard to share through all stages. meanwhile, the performance of previous framework suffers from error propagation problem. the above information misleading leads to the information gap between decision and generation. b) our framework is a one-stage end-to-end model. to bridge the information gap, the fused answer generation directly generates the decisions or follow-up questions with the shared entailment representation enhanced by activated entailment reasoning. however, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages. to tackle the above problem, we propose a novel one-stage end-to-end framework, called entailment fused-t5 (eft) to bridge the information gap between decision-making and question generation in a global understanding manner. specifically, our model consists of a universal encoder and a duplex decoder. the decoder consists of an activated entailment reasoning decoder and a fused answer generation decoder. the implicit reasoning chains of both decision-making and question generation in the multi-fused answer generation decoder are explicitly supervised by activated entailment reasoning through the shared entailment representation of our encoder. moreover, a relevance-diversity fusion strategy is utilized to further improve the implicit reasoning abilities among the multiple retrieved rules for the fused answer generation decoder through the implicit ranking method. thus, our model can reason in a global understanding manner. the extensive results, as illustrated in figure 3 , demonstrate that our proposed framework eft achieves new stateof-the-art performance on the or-sharc benchmark. our contributions are summarized as follows: conversational machine reading comprehension cmrc @xcite is the hybrid form of conversation-based reading comprehension, which requires the machines to make a decision or generate a follow-up question based on rule text, user scenario, user question and dialogue history. in this paper, we focus on the open-retrieval conversational machine reading (ocmrc) task @xcite , which further extends the cmrc task into a real-life scenario. machines are required to first retrieve related rule texts in a knowledge base based on user questions and user scenarios, then machines are required to make a decision of yes/no/inquire, or a follow-up question if the decision is inquire based on the relevant rule texts, user scenario, user question and dialogue history. due to the hybrid-form task, the previous methods @xcite @xcite typically adopt pipeline architectures, including decision-making, span extraction and question phrasing. various kinds of entailment reason-ing strategies are proposed to improve the performance of decision-making. despite the effectiveness of entailment reasoning, the performance is still limited because of the information gap between decision-making and question generation. recent studies @xcite explored entailment reasoning sharing methods to reduce the gap between decision-making and question generation, but the performance is limited due to its frame flaws. in this paper, we propose a novel one-stage end-to-end model, called entailment fused-t5 (eft), the details are written in the next sections. ## methods in open-retrieval cmrc, the machines are first required to retrieve related rule texts in a knowledge base, given user question and user scenario. then machines are required to make decisions or generate follow-up questions based on retrieved rule texts, user scenario, user question and dialogue history. thus, we conduct a retriever to first retrieve related rule texts from the knowledge database, and then generate the final answer through our end-to-end reader eft. the training procedure is shown in algorithm 1. ## conclusion in this paper, we propose a novel end-to-end framework, called eft, to bridge the information gap between decision-making and question generation through the shared entailment representation in a global understanding manner. extensive experimental results on the or-sharc benchmark demonstrate the effectiveness of our proposed framework eft. in our analysis, the implicit reasoning ability of both decision-making and question generation is significantly improved by sharing external explicit entailment knowledge through our novel framework eft.