Multi-party dialogue machine reading comprehension (MRC) raises an even more
challenging understanding goal on dialogue with more than two involved
speakers, compared with the traditional plain passage style MRC. To accurately
perform the question-answering (QA) task according to such multi-party
dialogue, models have to handle fundamentally different discourse relationships
from common non-dialogue plain text, where discourse relations are supposed to
connect two far apart utterances in a linguistics-motivated way.To further
explore the role of such unusual discourse structure on the correlated QA task
in terms of MRC, we propose the first multi-task model for jointly performing
QA and discourse parsing (DP) on the multi-party dialogue MRC task. Our
proposed model is evaluated on the latest benchmark Molweni, whose results
indicate that training with complementary tasks indeed benefits not only QA
task, but also DP task itself. We further find that the joint model is
distinctly stronger when handling longer dialogues which again verifies the
necessity of DP in the related MRC.

提出了一个用于多方对话机器阅读理解 (MRC) 的多任务模型，结合问答 (QA) 和话语解析 (DP) 任务，在 Molweni 基准测试中证明了多任务训练不仅受益于 QA 任务，也有助于 DP 任务，当处理更长的对话时，联合模型表现得更强，验证了 DP 在相关 MRC 中的必要性。

使用话语解析的多任务对话理解

Multi-tasking Dialogue Comprehension with Discourse Parsing

Multi-party dialogue machine reading comprehension (MRC) brings tremendous
challenge since it involves multiple speakers at one dialogue, resulting in
intricate speaker information flows and noisy dialogue contexts. To alleviate
such difficulties, previous models focus on how to incorporate these
information using complex graph-based modules and additional manually labeled
data, which is usually rare in real scenarios. In this paper, we design two
labour-free self- and pseudo-self-supervised prediction tasks on speaker and
key-utterance to implicitly model the speaker information flows, and capture
salient clues in a long dialogue. Experimental results on two benchmark
datasets have justified the effectiveness of our method over competitive
baselines and current state-of-the-art models.

本文介绍了一种应对多方对话机器阅读理解中复杂讲话信息和嘈杂的对话语境等困难的方法，通过使用两种无需标注的自监督预测任务来隐式地建模讲话者信息流和抓取长对话中显著的线索。实验结果表明，本方法比竞争基准和当前最先进的模型更为有效。

基于自监督和伪自监督的多方对话阅读理解中发言者和关键语句的预测

Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance  for Multi-party Dialogue Reading Comprehension

Previous works related to automatic personality recognition focus on using
traditional classification models with linguistic features. However, attentive
neural networks with contextual embeddings, which have achieved huge success in
text classification, are rarely explored for this task. In this project, we
have two major contributions. First, we create the first dialogue-based
personality dataset, FriendsPersona, by annotating 5 personality traits of
speakers from Friends TV Show through crowdsourcing. Second, we present a novel
approach to automatic personality recognition using pre-trained contextual
embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely
improve the state-of-art results on the monologue Essays dataset by 2.49%, and
establish a solid benchmark on our FriendsPersona. By comparing results in two
datasets, we demonstrate the challenges of modeling personality in multi-party
dialogue.

本文创建了首个基于对话的个性化数据集，并使用预先训练的语境嵌入和注意力神经网络等方法探索自动个性识别新方法，通过对两种数据集的比较，展示了在多方对话中建模个性所面临的挑战。