107 research outputs found
Explicit Contextual Semantics for Text Comprehension
Who did what to whom is a major focus in natural language understanding,
which is right the aim of semantic role labeling (SRL) task. Despite of sharing
a lot of processing characteristics and even task purpose, it is surprisingly
that jointly considering these two related tasks was never formally reported in
previous work. Thus this paper makes the first attempt to let SRL enhance text
comprehension and inference through specifying verbal predicates and their
corresponding semantic roles. In terms of deep learning models, our embeddings
are enhanced by explicit contextual semantic role labels for more fine-grained
semantics. We show that the salient labels can be conveniently added to
existing models and significantly improve deep learning models in challenging
text comprehension tasks. Extensive experiments on benchmark machine reading
comprehension and inference datasets verify that the proposed semantic learning
helps our system reach new state-of-the-art over strong baselines which have
been enhanced by well pretrained language models from the latest progress.Comment: Proceedings of the 33nd Pacific Asia Conference on Language,
Information and Computation (PACLIC 33
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
This study considers the task of machine reading at scale (MRS) wherein,
given a question, a system first performs the information retrieval (IR) task
of finding relevant passages in a knowledge source and then carries out the
reading comprehension (RC) task of extracting an answer span from the passages.
Previous MRS studies, in which the IR component was trained without considering
answer spans, struggled to accurately find a small number of relevant passages
from a large set of passages. In this paper, we propose a simple and effective
approach that incorporates the IR and RC tasks by using supervised multi-task
learning in order that the IR component can be trained by considering answer
spans. Experimental results on the standard benchmark, answering SQuAD
questions using the full Wikipedia as the knowledge source, showed that our
model achieved state-of-the-art performance. Moreover, we thoroughly evaluated
the individual contributions of our model components with our new Japanese
dataset and SQuAD. The results showed significant improvements in the IR task
and provided a new perspective on IR for RC: it is effective to teach which
part of the passage answers the question rather than to give only a relevance
score to the whole passage.Comment: 10 pages, 6 figure. Accepted as a full paper at CIKM 201
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