ET 5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022)

Abstract: Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extraction if required by the above decision; (3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available.

## introduction conversational machine reading comprehension (cmrc) @xcite aims to assist machines to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. specifically, the machine needs to reason for decision making and question generation by interacting through rule document, user question, user scenario, and dialogue history. as an example shown in figure 1, after fully interacting with complicated context information, the machine makes a decision of yes/no/inquire/irrelevant, and then generates a question under the inquire decision. existing researches @xcite @xcite @xcite mainly aim to capture the interactions among the complicated inputs, and achieve promising results by conducting various finefor pipeline i, decision making and span extraction models share the encoder but suffer from the problem of noisy span extraction. (b) for pipeline ii, the three stages are handled completely separately, and there is no information sharing among the three stages. (c) our framework is an end-to-end framework with a shared encoder and a duplex decoder. the duplex decoder contains an entailment reasoning decoder and answer generation decoder, both the information of entailment reasoning and answer generation are shared through the common encoder. both decisions and follow-up questions will be generated via answer generation decoder directly. however, for nearly all these methods @xcite @xcite , the span extraction and question rephrasing steps can't fully exploit the finegrained entailment reasoning information in decision making step. for pipeline ii, these methods @xcite do not share entailment reasoning information among decision-making, span extraction, and question phrasing at all. for pipeline i, these methods @xcite ) only approximate share the information through noisy span extraction. both of them enlarge the information gap between decision making and question rephrasing, and seriously affect the performance of question generation. to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning t5 (et5). specifically, the proposed framework consists of a text-to-text transformer and an additional entailment reasoning decoder. the original decoder in the text-to-text transformer will directly generate either decision or follow-up question based on the shared encoder enhanced by entailment reasoning. the entailment reasoning decoder can be configured with different entailment reasoning strategies. despite the lightweight of our proposed framework, experimental results show that et5 achieves new state-of-the-art results on the sharc leaderboard with the bleu-4 score of 55.2 and significantly improves the generalization performance of question generation. our contributions are summarized as follows: ## related work conversation-based reading comprehension @xcite @xcite @xcite extends the context with dialogue history, which is formed to simulate the communication scene in real life. most of them are ideal subtasks, either span-based qa tasks @xcite or multi-choice tasks @xcite . we focus on the task @xcite that deal with real-world complexities, where the machine needs to make decisions or ask questions to keep the conversa-tion going. this task @xcite is called conversational machine reading comprehension (cmrc), which requires the machine to have the inference ability to capture the interactions among rule document, user question, user scenario, and dialogue history. recent studies @xcite @xcite in cmrc are generally utilized to match the relationship between the various information. e 3 (zhong and zettlemoyer, 2019) first investigates the importance of clarifying the different rule units for entailment reasoning. different entailment reasoning strategies @xcite with fine-grained reasoning units are further proposed to improve the abilities of entailment reasoning. in addition, discourse relationships between finegrained reasoning units are utilized to model the discourse graph @xcite . these methods typically adopt pipeline architectures, discern @xcite first discovers the unbalance and noisy problems of pipeline i conducted by e 3 (zhong and zettlemoyer, 2019) and emt @xcite , then solves them by utilizing pipeline ii to process the three stages separately. however, due to the pipeline's inability to make full use of the entailment information, both of the above pipeline structures have the problem of information gap @xcite