Causal relation extraction of biomedical entities is one of the most complex
tasks in biomedical text mining, which involves two kinds of information:
entity relations and entity functions. One feasible approach is to take
relation extraction and function detection as two independent sub-tasks.
However, this separate learning method ignores the intrinsic correlation
between them and leads to unsatisfactory performance. In this paper, we propose
a joint learning model, which combines entity relation extraction and entity
function detection to exploit their commonality and capture their
inter-relationship, so as to improve the performance of biomedical causal
relation extraction. Meanwhile, during the model training stage, different
function types in the loss function are assigned different weights.
Specifically, the penalty coefficient for negative function instances increases
to effectively improve the precision of function detection. Experimental
results on the BioCreative-V Track 4 corpus show that our joint learning model
outperforms the separate models in BEL statement extraction, achieving the F1
scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations,
respectively. This demonstrates that our joint learning system reaches the
state-of-the-art performance in Stage 2 compared with other systems.

本文提出了一种基于联合学习模型的生物医学因果关系提取方法，将实体关系提取和实体功能检测结合起来，以提高因果关系提取的性能，并且在模型训练阶段，为不同的功能类型分配不同的权重，实验结果表明，与其他系统相比，我们的联合学习系统在 BEL 语句提取中取得了最先进的表现。

基于联合学习的生物医学文献因果关系抽取

Joint Learning-based Causal Relation Extraction from Biomedical Literature

Character linking, the task of linking mentioned people in conversations to
the real world, is crucial for understanding the conversations. For the
efficiency of communication, humans often choose to use pronouns (e.g., "she")
or normal phrases (e.g., "that girl") rather than named entities (e.g.,
"Rachel") in the spoken language, which makes linking those mentions to real
people a much more challenging than a regular entity linking task. To address
this challenge, we propose to incorporate the richer context from the
coreference relations among different mentions to help the linking. On the
other hand, considering that finding coreference clusters itself is not a
trivial task and could benefit from the global character information, we
propose to jointly solve these two tasks. Specifically, we propose C$^2$, the
joint learning model of Coreference resolution and Character linking. The
experimental results demonstrate that C$^2$ can significantly outperform
previous works on both tasks. Further analyses are conducted to analyze the
contribution of all modules in the proposed model and the effect of all
hyper-parameters.

提出了一个核心关系联结的联合学习模型，可解决对话中提到的人物和实际人物之间的联结问题，实验证明该模型在角色联结和核心关系解决方面都优于之前的工作。

多方会话的联合指代消解和人物链接

Joint Coreference Resolution and Character Linking for Multiparty  Conversation

Community question answering (CQA) gains increasing popularity in both
academy and industry recently. However, the redundancy and lengthiness issues
of crowdsourced answers limit the performance of answer selection and lead to
reading difficulties and misunderstandings for community users. To solve these
problems, we tackle the tasks of answer selection and answer summary generation
in CQA with a novel joint learning model. Specifically, we design a
question-driven pointer-generator network, which exploits the correlation
information between question-answer pairs to aid in attending the essential
information when generating answer summaries. Meanwhile, we leverage the answer
summaries to alleviate noise in original lengthy answers when ranking the
relevancy degrees of question-answer pairs. In addition, we construct a new
large-scale CQA corpus, WikiHowQA, which contains long answers for answer
selection as well as reference summaries for answer summarization. The
experimental results show that the joint learning method can effectively
address the answer redundancy issue in CQA and achieves state-of-the-art
results on both answer selection and text summarization tasks. Furthermore, the
proposed model is shown to be of great transferring ability and applicability
for resource-poor CQA tasks, which lack of reference answer summaries.

该研究利用新颖的联合学习模型解决 CQA 中答案冗余和长度问题，设计了一个以问题为导向的指针生成网络，同时利用答案给长回答降噪，构建了一个包含长回答和参考摘要的大规模 CQA 语料库 WikiHowQA，实验证明该方法可以有效地解决 CQA 中的冗余问题，并取得了在答案选择和文本摘要任务上的最新成果。