24 research outputs found
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
We present a novel neural architecture for the Argument Reasoning
Comprehension task of SemEval 2018. It is a simple neural network consisting of
three parts, collectively judging whether the logic built on a set of given
sentences (a claim, reason, and warrant) is plausible or not. The model
utilizes contextualized word vectors pre-trained on large machine translation
(MT) datasets as a form of transfer learning, which can help to mitigate the
lack of training data. Quantitative analysis shows that simply leveraging LSTMs
trained on MT datasets outperforms several baselines and non-transferred
models, achieving accuracies of about 70% on the development set and about 60%
on the test set.Comment: SemEval 201
Ranking Significant Discrepancies in Clinical Reports
Medical errors are a major public health concern and a leading cause of death
worldwide. Many healthcare centers and hospitals use reporting systems where
medical practitioners write a preliminary medical report and the report is
later reviewed, revised, and finalized by a more experienced physician. The
revisions range from stylistic to corrections of critical errors or
misinterpretations of the case. Due to the large quantity of reports written
daily, it is often difficult to manually and thoroughly review all the
finalized reports to find such errors and learn from them. To address this
challenge, we propose a novel ranking approach, consisting of textual and
ontological overlaps between the preliminary and final versions of reports. The
approach learns to rank the reports based on the degree of discrepancy between
the versions. This allows medical practitioners to easily identify and learn
from the reports in which their interpretation most substantially differed from
that of the attending physician (who finalized the report). This is a crucial
step towards uncovering potential errors and helping medical practitioners to
learn from such errors, thus improving patient-care in the long run. We
evaluate our model on a dataset of radiology reports and show that our approach
outperforms both previously-proposed approaches and more recent language models
by 4.5% to 15.4%.Comment: ECIR 2020 (short
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Context plays an important role in human language understanding, thus it may
also be useful for machines learning vector representations of language. In
this paper, we explore an asymmetric encoder-decoder structure for unsupervised
context-based sentence representation learning. We carefully designed
experiments to show that neither an autoregressive decoder nor an RNN decoder
is required. After that, we designed a model which still keeps an RNN as the
encoder, while using a non-autoregressive convolutional decoder. We further
combine a suite of effective designs to significantly improve model efficiency
while also achieving better performance. Our model is trained on two different
large unlabelled corpora, and in both cases the transferability is evaluated on
a set of downstream NLP tasks. We empirically show that our model is simple and
fast while producing rich sentence representations that excel in downstream
tasks