This paper addresses the problem of dialogue reasoning with contextualized
commonsense inference. We curate CICERO, a dataset of dyadic conversations with
five types of utterance-level reasoning-based inferences: cause, subsequent
event, prerequisite, motivation, and emotional reaction. The dataset contains
53,105 of such inferences from 5,672 dialogues. We use this dataset to solve
relevant generative and discriminative tasks: generation of cause and
subsequent event; generation of prerequisite, motivation, and listener's
emotional reaction; and selection of plausible alternatives. Our results
ascertain the value of such dialogue-centric commonsense knowledge datasets. It
is our hope that CICERO will open new research avenues into commonsense-based
dialogue reasoning.

本文介绍了解决上下文中常识性推理对话推理问题的方法，并介绍了通过 CICERO 数据集解决相关机器学习生成和判别任务的结果，提出 CICERO 对话性常识知识数据集的价值，并为基于常识的对话推理开辟新的研究方向。

CICERO：对话中语境化常识推理的数据集

CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

Despite the success of neural dialogue systems in achieving high performance
on the leader-board, they cannot meet users' requirements in practice, due to
their poor reasoning skills. The underlying reason is that most neural dialogue
models only capture the syntactic and semantic information, but fail to model
the logical consistency between the dialogue history and the generated
response. Recently, a new multi-turn dialogue reasoning task has been proposed,
to facilitate dialogue reasoning research. However, this task is challenging,
because there are only slight differences between the illogical response and
the dialogue history. How to effectively solve this challenge is still worth
exploring. This paper proposes a Fine-grained Comparison Model (FCM) to tackle
this problem. Inspired by human's behavior in reading comprehension, a
comparison mechanism is proposed to focus on the fine-grained differences in
the representation of each response candidate. Specifically, each candidate
representation is compared with the whole history to obtain a history
consistency representation. Furthermore, the consistency signals between each
candidate and the speaker's own history are considered to drive a model to
prefer a candidate that is logically consistent with the speaker's history
logic. Finally, the above consistency representations are employed to output a
ranking list of the candidate responses for multi-turn dialogue reasoning.
Experimental results on two public dialogue datasets show that our method
obtains higher ranking scores than the baseline models.

本文提出了 Fine-grained Comparison Model，该模型的比较机制可以关注每个响应候选项表示的微小差异，并通过将相关信号考虑到驱动模型偏好与演讲者历史逻辑一致的候选项来解决多轮对话推理中响应不一致的问题，实验证明该方法比基准模型具有更高的排名得分

FCM: 多轮对话推理的精细比较模型

FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning

Conversational machine comprehension requires deep understanding of the
dialogue flow, and the prior work proposed FlowQA to implicitly model the
context representations in reasoning for better understanding. This paper
proposes to explicitly model the information gain through dialogue reasoning in
order to allow the model to focus on more informative cues. The proposed model
achieves state-of-the-art performance in a conversational QA dataset QuAC and
sequential instruction understanding dataset SCONE, which shows the
effectiveness of the proposed mechanism and demonstrates its capability of
generalization to different QA models and tasks.

本文提出了一种显式建模对话推理信息增益的方法来更好地理解对话流程和理解其背后的上下文，其在 QuAC 和 SCONE 数据集上取得最先进性能，展示了该方法的有效性和其推广到不同 QA 模型和任务的能力。