3,317 research outputs found
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning
This paper focuses on how to take advantage of external relational knowledge
to improve machine reading comprehension (MRC) with multi-task learning. Most
of the traditional methods in MRC assume that the knowledge used to get the
correct answer generally exists in the given documents. However, in real-world
task, part of knowledge may not be mentioned and machines should be equipped
with the ability to leverage external knowledge. In this paper, we integrate
relational knowledge into MRC model for commonsense reasoning. Specifically,
based on a pre-trained language model (LM). We design two auxiliary
relation-aware tasks to predict if there exists any commonsense relation and
what is the relation type between two words, in order to better model the
interactions between document and candidate answer option. We conduct
experiments on two multi-choice benchmark datasets: the SemEval-2018 Task 11
and the Cloze Story Test. The experimental results demonstrate the
effectiveness of the proposed method, which achieves superior performance
compared with the comparable baselines on both datasets.Comment: Accepted at CIKM'19, 4 page
Incorporating Structured Commonsense Knowledge in Story Completion
The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant
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