6 research outputs found
Hashing based Answer Selection
Answer selection is an important subtask of question answering (QA), where
deep models usually achieve better performance. Most deep models adopt
question-answer interaction mechanisms, such as attention, to get vector
representations for answers. When these interaction based deep models are
deployed for online prediction, the representations of all answers need to be
recalculated for each question. This procedure is time-consuming for deep
models with complex encoders like BERT which usually have better accuracy than
simple encoders. One possible solution is to store the matrix representation
(encoder output) of each answer in memory to avoid recalculation. But this will
bring large memory cost. In this paper, we propose a novel method, called
hashing based answer selection (HAS), to tackle this problem. HAS adopts a
hashing strategy to learn a binary matrix representation for each answer, which
can dramatically reduce the memory cost for storing the matrix representations
of answers. Hence, HAS can adopt complex encoders like BERT in the model, but
the online prediction of HAS is still fast with a low memory cost. Experimental
results on three popular answer selection datasets show that HAS can outperform
existing models to achieve state-of-the-art performance
Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning
Chinese medical question-answer matching is more challenging than the
open-domain question answer matching in English. Even though the deep learning
method has performed well in improving the performance of question answer
matching, these methods only focus on the semantic information inside
sentences, while ignoring the semantic association between questions and
answers, thus resulting in performance deficits. In this paper, we design a
series of interactive sentence representation learning models to tackle this
problem. To better adapt to Chinese medical question-answer matching and take
the advantages of different neural network structures, we propose the Crossed
BERT network to extract the deep semantic information inside the sentence and
the semantic association between question and answer, and then combine with the
multi-scale CNNs network or BiGRU network to take the advantage of different
structure of neural networks to learn more semantic features into the sentence
representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show
that our model significantly outperforms all the existing state-of-the-art
models of Chinese medical question answer matching
A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems
Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters