32,570 research outputs found
Exploiting Sentence Embedding for Medical Question Answering
Despite the great success of word embedding, sentence embedding remains a
not-well-solved problem. In this paper, we present a supervised learning
framework to exploit sentence embedding for the medical question answering
task. The learning framework consists of two main parts: 1) a sentence
embedding producing module, and 2) a scoring module. The former is developed
with contextual self-attention and multi-scale techniques to encode a sentence
into an embedding tensor. This module is shortly called Contextual
self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two
scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association
Scoring (SAS). SMS measures similarity while SAS captures association between
sentence pairs: a medical question concatenated with a candidate choice, and a
piece of corresponding supportive evidence. The proposed framework is examined
by two Medical Question Answering(MedicalQA) datasets which are collected from
real-world applications: medical exam and clinical diagnosis based on
electronic medical records (EMR). The comparison results show that our proposed
framework achieved significant improvements compared to competitive baseline
approaches. Additionally, a series of controlled experiments are also conducted
to illustrate that the multi-scale strategy and the contextual self-attention
layer play important roles for producing effective sentence embedding, and the
two kinds of scoring strategies are highly complementary to each other for
question answering problems.Comment: 8 page
Question Dependent Recurrent Entity Network for Question Answering
Question Answering is a task which requires building models capable of
providing answers to questions expressed in human language. Full question
answering involves some form of reasoning ability. We introduce a neural
network architecture for this task, which is a form of , that
recognizes entities and their relations to answers through a focus attention
mechanism. Our model is named
and extends by exploiting aspects of the question
during the memorization process. We validate the model on both synthetic and
real datasets: the question answering dataset and the $CNN\ \&\ Daily\
Newsreading\ comprehension$ dataset. In our experiments, the models achieved
a State-of-The-Art in the former and competitive results in the latter.Comment: 14 page
Teaching Machines to Read and Comprehend
Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In this work we define a new methodology that resolves this
bottleneck and provides large scale supervised reading comprehension data. This
allows us to develop a class of attention based deep neural networks that learn
to read real documents and answer complex questions with minimal prior
knowledge of language structure.Comment: Appears in: Advances in Neural Information Processing Systems 28
(NIPS 2015). 14 pages, 13 figure
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
In this paper, we present a two stage model for multi-hop question answering.
The first stage is a hierarchical graph network, which is used to reason over
multi-hop question and is capable to capture different levels of granularity
using the nature structure(i.e., paragraphs, questions, sentences and entities)
of documents. The reasoning process is convert to node classify task(i.e.,
paragraph nodes and sentences nodes). The second stage is a language model
fine-tuning task. In a word, stage one use graph neural network to select and
concatenate support sentences as one paragraph, and stage two find the answer
span in language model fine-tuning paradigm.Comment: the experience result is not as good as I excep
Generating Distractors for Reading Comprehension Questions from Real Examinations
We investigate the task of distractor generation for multiple choice reading
comprehension questions from examinations. In contrast to all previous works,
we do not aim at preparing words or short phrases distractors, instead, we
endeavor to generate longer and semantic-rich distractors which are closer to
distractors in real reading comprehension from examinations. Taking a reading
comprehension article, a pair of question and its correct option as input, our
goal is to generate several distractors which are somehow related to the
answer, consistent with the semantic context of the question and have some
trace in the article. We propose a hierarchical encoder-decoder framework with
static and dynamic attention mechanisms to tackle this task. Specifically, the
dynamic attention can combine sentence-level and word-level attention varying
at each recurrent time step to generate a more readable sequence. The static
attention is to modulate the dynamic attention not to focus on question
irrelevant sentences or sentences which contribute to the correct option. Our
proposed framework outperforms several strong baselines on the first prepared
distractor generation dataset of real reading comprehension questions. For
human evaluation, compared with those distractors generated by baselines, our
generated distractors are more functional to confuse the annotators.Comment: AAAI201
- …