Meta-Learning Siamese Network for Few-Shot Text Classification

Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO). Despite the success of PROTO, there still exist three main problems: (1) ignore the randomness of the sampled support sets when computing prototype vectors; (2) disregard the importance of labeled samples; (3) construct meta-tasks in a purely random manner. In this paper, we propose a Meta-Learning Siamese Network, namely, Meta-SN, to address these issues. Specifically, instead of computing prototype vectors from the sampled support sets, Meta-SN utilizes external knowledge (e.g. class names and descriptive texts) for class labels, which is encoded as the low-dimensional embeddings of prototype vectors. In addition, Meta-SN presents a novel sampling strategy for constructing meta-tasks, which gives higher sampling probabilities to hard-to-classify samples. Extensive experiments are conducted on six benchmark datasets to show the clear superiority of Meta-SN over other state-of-the-art models. For reproducibility, all the datasets and codes are provided at https://github.com/hccngu/Meta-SN.

EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition

Printed Mathematical expression recognition (PMER) aims to transcribe a printed mathematical expression image into a structural expression, such as LaTeX expression. It is a crucial task for many applications, including automatic question recommendation, automatic problem solving and analysis of the students, etc. Currently, the mainstream solutions rely on solving image captioning tasks, all addressing image summarization. As such, these methods can be suboptimal for solving MER problem. In this paper, we propose a new method named EDSL, shorted for encoder-decoder with symbol-level features, to identify the printed mathematical expressions from images. The symbol-level image encoder of EDSL consists of segmentation module and reconstruction module. By performing segmentation module, we identify all the symbols and their spatial information from images in an unsupervised manner. We then design a novel reconstruction module to recover the symbol dependencies after symbol segmentation. Especially, we employ a position correction attention mechanism to capture the spatial relationships between symbols. To alleviate the negative impact from long output, we apply the transformer model for transcribing the encoded image into the sequential and structural output. We conduct extensive experiments on two real datasets to verify the effectiveness and rationality of our proposed EDSL method. The experimental results have illustrated that EDSL has achieved 92.7\% and 89.0\% in evaluation metric Match, which are 3.47\% and 4.04\% higher than the state-of-the-art method. Our code and datasets are available at https://github.com/abcAnonymous/EDSL .