In open-retrieval conversational machine reading (OR-CMR) task, machines are
required to do multi-turn question answering given dialogue history and a
textual knowledge base. Existing works generally utilize two independent
modules to approach this problem's two successive sub-tasks: first with a
hard-label decision making and second with a question generation aided by
various entailment reasoning methods. Such usual cascaded modeling is
vulnerable to error propagation and prevents the two sub-tasks from being
consistently optimized. In this work, we instead model OR-CMR as a unified
text-to-text task in a fully end-to-end style. Experiments on the OR-ShARC
dataset show the effectiveness of our proposed end-to-end framework on both
sub-tasks by a large margin, achieving new state-of-the-art results. Further
ablation studies support that our framework can generalize to different
backbone models.

本文介绍了一种基于完全端到端的方式将开放检索对话式机器阅读（OR-CMR）建模为一个统一的文本到文本任务，并在 OR-ShARC 数据集上展示了其在两个子任务上的显著有效性和创新的最先进结果。

面向端到端的开放对话式机器阅读

Towards End-to-End Open Conversational Machine Reading

In this paper, we propose a novel Knowledge-based Embodied Question Answering
(K-EQA) task, in which the agent intelligently explores the environment to
answer various questions with the knowledge. Different from explicitly
specifying the target object in the question as existing EQA work, the agent
can resort to external knowledge to understand more complicated question such
as "Please tell me what are objects used to cut food in the room?", in which
the agent must know the knowledge such as "knife is used for cutting food".
To address this K-EQA problem, a novel framework based on neural program
synthesis reasoning is proposed, where the joint reasoning of the external
knowledge and 3D scene graph is performed to realize navigation and question
answering. Especially, the 3D scene graph can provide the memory to store the
visual information of visited scenes, which significantly improves the
efficiency for the multi-turn question answering. Experimental results have
demonstrated that the proposed framework is capable of answering more
complicated and realistic questions in the embodied environment. The proposed
method is also applicable to multi-agent scenarios.

本文提出了一种基于知识的具身问答 (Knowledge-based Embodied Question Answering, K-EQA) 任务，其中智能体通过利用知识来探索环境以回答各种问题。为解决这一问题，本文提出了一种基于神经程序合成推理的新框架，同时进行外部知识和 3D 场景图的联合推理，以实现导航和问答。实验结果表明，该框架能够在具身环境中回答更加复杂和现实的问题，并且该方法也适用于多智能体场景。

基于知识的具身化问答

Knowledge-based Embodied Question Answering

In this paper, we propose a new paradigm for the task of entity-relation
extraction. We cast the task as a multi-turn question answering problem, i.e.,
the extraction of entities and relations is transformed to the task of
identifying answer spans from the context. This multi-turn QA formalization
comes with several key advantages: firstly, the question query encodes
important information for the entity/relation class we want to identify;
secondly, QA provides a natural way of jointly modeling entity and relation;
and thirdly, it allows us to exploit the well developed machine reading
comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora
demonstrate that the proposed paradigm significantly outperforms previous best
models. We are able to obtain the state-of-the-art results on all of the ACE04,
ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets
to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively. Additionally, we
construct a newly developed dataset RESUME in Chinese, which requires
multi-step reasoning to construct entity dependencies, as opposed to the
single-step dependency extraction in the triplet exaction in previous datasets.
The proposed multi-turn QA model also achieves the best performance on the
RESUME dataset.

本文提出了一种新的实体关系抽取范式，将该任务作为多轮问答问题，利用机器阅读理解模型，实现从语境中识别答案组成实体和关系类别的任务，并在 ACE 和 CoNLL04 语料库上进行实验，取得了显著的性能提升。此外，我们还构建了一个新的 RESUME 数据集，该数据集与以前的三元组数据集不同，需要进行多步推理建立实体间的依赖关系。