Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge, with the scarcity of data in few-shot scenarios further exacerbating these issues by hindering effective data augmentation in the latent space. In this paper, we propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation. From these descriptions, we introduce a bi-encoder retrieval training paradigm to enrich both sample and class representation learning. Leveraging these enhanced representations, we design a retrieval-based prediction method where each sample "retrieves" the best fitting relation via a reciprocal rank fusion score that integrates both relation description vectors and class prototypes. Extensive experiments on multiple datasets demonstrate that our method significantly advances the state-of-the-art by maintaining robust performance across sequential tasks, effectively addressing catastrophic forgetting.

Anh Duc Le, Nam Le Hai, Thanh Xuan Nguyen, Linh Ngo Van, Nguyen Thi Ngoc Diep, Sang Dinh, Thien Huu Nguyen. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

This paper proposes a method for speeding upparsing process for recognizing online handwritten mathematicalexpressions (OHME). We prune infeasible partitions in theparsing table to reduce the time for the parsing process. Lowscore partitions are candidates for pruning. Our method can beapplied for any parsing algorithms that use score functions. Inthis paper, we use a stroke order free system as a baseline system.The method is as follows. First, we analyze the scores ofpartitions in each row of the parsing table. Then, we determine athreshold for each row to prune low score partitions. Finally, weemploy these thresholds to prune low score partitions on thebaseline recognition system. The results of evaluations of ourmethod on the CROHME 2014 database show that therecognition process is speeded up by 3.46 times and 4.97 timeswhile recognition rate is reduced only 0.31 point and 0.71 point,respectively.

Motivated by recent successes in neural machine translation and image caption generation, we present an end-to-end system to recognize Online Handwritten Mathematical Expressions (OHMEs). Our system has three parts: a convolution neural network for feature extraction, a bidirectional LSTM for encoding extracted features, and an LSTM and an attention model for generating target LaTex. For recognizing complex structures, our system needs large data for training. We propose local and global distortion models for generating OHMEs from the CROHME database. We evaluate the end-to-end system on the CROHME database and the generated databases. The experiential results show that the end-to-end system achieves 28.09% and 35.19% recognition rates on CROHME without and with the generated data, respectively.

This paper proposes a modified X-Y cut method for reordering strokes of online handwritten mathematical expression (ME) in order to make stroke order free recognition. To deal with overlapping, which causes problems in the X-Y cut method, we determine vertically ordered strokes by detecting vertical symbols and its upper/lower MEs. An upper ME and a lower ME are treated as MEs which are reordered recursively. Unordered strokes on the left side of a vertical symbol are reordered as horizontally ordered strokes. The remaining strokes are reordered recursively. The horizontally ordered strokes are reordered from left to right and the vertically ordered strokes are reordered from top to bottom. The results of evaluations of the reordering method on the CROHME 2014 database show that our ME recognition system incorporating this method outperforms all other systems that use only CROHME 2014 for training while the processing time is kept to a practical level.

Parsing process is the most important process in recognition of online handwritten mathematical expressions. There are two basic approaches: stroke order dependent (SOD) and stroke order free (SOF) approaches. The SOD approach depends on stroke order while the SOF approach is free from stroke order. Although both approaches have shown high recognition rates in recently competitions, there are a few of works that analyze the complexities of parsing algorithms in the same experimental conditions. In this work, we have tested and analyzed recognition rate, recognition speed and memory space required by parsing algorithms on CROHME 2014. SOF is slightly superior to SOD in recognition rate, but SOD is faster in processing time and lower in memory space than SOF.

This paper presents a semi-incremental recognition method for online handwritten mathematical expressions (MEs). The method reduces the waiting time after an ME is written until the result of recognition is output. Our method has two main processes, one is to process the latest stroke, the other is to find and correct wrong recognitions in the strokes up to the latest stroke. In the first process, the segmentation, recognition and Cocke-Younger-Kasami (CYK) algorithm are only executed for the latest stroke. In the second process, all the previous segmentations are updated if they are significantly changed after the latest stroke is input, and then, all the symbols related to the updated segmentations will be updated with their recognition scores. These changes are reflected into the CYK table. In addition, the waiting time is further reduced by employing multi-thread processes. Experiments on our data set show the effectiveness of this semi-incremental method which not only has higher recognition rate than our previous pure-incremental method but also keeps the waiting time unnoticeable.

This paper presents a system for recognizing online handwritten mathematical expressions (MEs) and improvement of structure analysis. We represent MEs in Context Free Grammars (CFGs) and employ the Cocke-Younger-Kasami (CYK) algorithm to parse 2D structure of on-line handwritten MEs and select the best interpretation in terms of symbol segmentation, recognition and structure analysis. We propose a method to learn structural relations from training patterns without any heuristic decisions by using two SVM models. We employ stroke order to reduce the complexity of the parsing algorithm. Moreover, we revise structure analysis. Even though CFG does not resolve ambiguities in some cases, our method still gives users a list of candidates that contain expecting result. We evaluate our method in the CROHME 2013 database and demonstrate the improvement of our system in recognition rate as well as processing time.