Emotion Cause Extraction in Conversations (ECEC) aims to extract the
utterances which contain the emotional cause in conversations. Most prior
research focuses on modelling conversational contexts with sequential encoding,
ignoring the informative interactions between utterances and
conversational-specific features for ECEC. In this paper, we investigate the
importance of discourse structures in handling utterance interactions and
conversationspecific features for ECEC. To this end, we propose a
discourse-aware model (DAM) for this task. Concretely, we jointly model ECEC
with discourse parsing using a multi-task learning (MTL) framework and
explicitly encode discourse structures via gated graph neural network (gated
GNN), integrating rich utterance interaction information to our model. In
addition, we use gated GNN to further enhance our ECEC model with
conversation-specific features. Results on the benchmark corpus show that DAM
outperform the state-of-theart (SOTA) systems in the literature. This suggests
that the discourse structure may contain a potential link between emotional
utterances and their corresponding cause expressions. It also verifies the
effectiveness of conversationalspecific features. The codes of this paper will
be available on GitHub.

该论文研究了在对话中提取情感原因的重要性，提出了一种基于话语结构的模型，利用多任务学习框架和门控图神经网络来建模和分析对话，加强了情感和原因表达之间的联系，并验证了模型的有效性。

会话中对话感情原因的认知感知抽取

Discourse-Aware Emotion Cause Extraction in Conversations

Visual relationship detection, as a challenging task used to find and
distinguish the interactions between object pairs in one image, has received
much attention recently. In this work, we propose a novel visual relationship
detection framework by deeply mining and utilizing relative location of
object-pair in every stage of the procedure. In both the stages, relative
location information of each object-pair is abstracted and encoded as auxiliary
feature to improve the distinguishing capability of object-pairs proposing and
predicate recognition, respectively; Moreover, one Gated Graph Neural
Network(GGNN) is introduced to mine and measure the relevance of predicates
using relative location. With the location-based GGNN, those non-exclusive
predicates with similar spatial position can be clustered firstly and then be
smoothed with close classification scores, thus the accuracy of top $n$ recall
can be increased further. Experiments on two widely used datasets VRD and VG
show that, with the deeply mining and exploiting of relative location
information, our proposed model significantly outperforms the current
state-of-the-art.

本文提出了一种基于物体对位置深度挖掘的视觉关系检测框架，通过使用 Gated Graph Neural Network 获取谓词间的相关性。在数据集 VRD 和 VG 上的实验结果表明，相对位置信息的挖掘和利用可以显著提高模型的性能。

基于相对位置挖掘的视觉关系检测

Visual Relationship Detection with Relative Location Mining

Natural question generation (QG) aims to generate questions from a passage
and an answer. In this paper, we propose a novel reinforcement learning (RL)
based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a
Graph2Seq generator where a novel Bidirectional Gated Graph Neural Network is
proposed to embed the passage, and a hybrid evaluator with a mixed objective
combining both cross-entropy and RL losses to ensure the generation of
syntactically and semantically valid text. The proposed model outperforms
previous state-of-the-art methods by a large margin on the SQuAD dataset.

本文提出了一种基于强化学习的图到序列模型，以生成语法和语义正确的自然问题，并取得了 SQuAD 数据集上大幅领先于之前最先进的方法的效果。