8 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
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions
Machine Reading Comprehension (MRC) with multiple-choice questions requires
the machine to read given passage and select the correct answer among several
candidates. In this paper, we propose a novel approach called Convolutional
Spatial Attention (CSA) model which can better handle the MRC with
multiple-choice questions. The proposed model could fully extract the mutual
information among the passage, question, and the candidates, to form the
enriched representations. Furthermore, to merge various attention results, we
propose to use convolutional operation to dynamically summarize the attention
values within the different size of regions. Experimental results show that the
proposed model could give substantial improvements over various
state-of-the-art systems on both RACE and SemEval-2018 Task11 datasets.Comment: 8 pages. Accepted as a conference paper at AAAI-19 Technical Trac
Multi-Perspective Fusion Network for Commonsense Reading Comprehension
Commonsense Reading Comprehension (CRC) is a significantly challenging task,
aiming at choosing the right answer for the question referring to a narrative
passage, which may require commonsense knowledge inference. Most of the
existing approaches only fuse the interaction information of choice, passage,
and question in a simple combination manner from a \emph{union} perspective,
which lacks the comparison information on a deeper level. Instead, we propose a
Multi-Perspective Fusion Network (MPFN), extending the single fusion method
with multiple perspectives by introducing the \emph{difference} and
\emph{similarity} fusion\deleted{along with the \emph{union}}. More
comprehensive and accurate information can be captured through the three types
of fusion. We design several groups of experiments on MCScript dataset
\cite{Ostermann:LREC18:MCScript} to evaluate the effectiveness of the three
types of fusion respectively. From the experimental results, we can conclude
that the difference fusion is comparable with union fusion, and the similarity
fusion needs to be activated by the union fusion. The experimental result also
shows that our MPFN model achieves the state-of-the-art with an accuracy of
83.52\% on the official test set