The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Math Problem Solver in Other Languages <s> This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an equation that represents the problem, and enables its (trivial) solution as shown in Figure 1. The paper analyzes the arithmetic-word problems “genre”, identifying seven categories of verbs used in such problems. ARIS learns to categorize verbs with 81.2% accuracy, and is able to solve 77.7% of the problems in a corpus of standard primary school test questions. We report the first learning results on this task without reliance on predefined templates and make our data publicly available. 1 <s> BIB001 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Math Problem Solver in Other Languages <s> This paper addresses the problem of solving arithmetic word problems that are directly stated in Chinese without any implicit quantity relations. This problem relates with the multiple challenging problems in artificial intelligent area. This paper proposes multiple new methods to overcome the challenges of the problem and forms a four-step algorithm. The first step is to parse the input math problem into phrases with property annotation. The second one is to use a pool of the semantic models to convert a math problem into a set of high-order equations. The third one is a procedure to solve the set of equations. The last step is to convert the machine solution into a humanoid solution. The experimental results on 140 arithmetic word problems from the text books show that the proposed method has a very good potential. <s> BIB002 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Math Problem Solver in Other Languages <s> The extraction of implicit quantity relations is one of critical steps of building the machine solver for solving arithmetic word problems. Extracting implicit quantity relations is a challenge research problem as partial problem of natural language understanding, which has no mature solution method. To address this challenge, the existing work in the extraction of quantity relations mainly relies on creating problem solving framework and extracting system of linear equations. However, these existing methods can solve the relatively simple problems due to they have no methods to extract implicit quality relations. This paper proposes a novel method for extracting implicit quantity relations, which is achieved by the process of Chinese phrase parse, classification and instantiation method of required general implicit quantity relations with semantic models. A set of experiments were conducted to classify the arithmetic word problems, and an example was taken to illustrate the proposed method of identifying general implicit quantity relations. The experimental results demonstrate that the proposed method is effective in extracting implicit quantity relations. <s> BIB003 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Math Problem Solver in Other Languages <s> Abstract The recent evolution in Natural Language Processing (NLP) and machine learning have played a crucial role in the development of solving word problems written in human language. This paper, to the best of our knowledge, presents the first attempt of automatically solving Arabic arithmetic word problems. In addition, as part of this work, we prepared an Arabic annotated dataset by translating a standard arithmetic word problems English dataset (AddSub Dataset). The AddSub dataset has been used by several researchers to evaluate their models for English arithmetic word problems. The proposed algorithm relies on our automatic verbs learning approach based on the training dataset. Moreover, the algorithm utilizes various NLP tools to assign objects to problem states until reaching to the goal state such as Stanford Parser, Named Entity Recognition (NER), and Cosine Metric Distance. Our approach overcomes various issues such as tracking both entities and their related results during the transfer process as well as dealing with different forms of the same verb. The performance evaluation process showed promising results resolving 80.78% of the problems. On the other hand, there are still several areas that can be extended and improved. For instance, the lack of common knowledge, presence of irrelevant information, and quantity conversions. <s> BIB004
Solving math word problems in other languages also attracts research attention. Yu et al. addressed the equation set problem solver in Chinese BIB002 , BIB003 . A pool of rule-based semantic models are crafted to map the sentences in Chinese text into equation templates. The experiments were conducted on a very small-scale dataset with 104 problems. Recently, there has been the first attempt to solve Arabic arithmetic word problems BIB004 . Its test dataset was collected by translating the AI2 dataset BIB001 from English to Arabic. The proposed techniques also rely on the verb categorization, similar to those proposed in BIB001 , except that customization for the Arabic language needs to be made for the tasks of syntactic parser and named entity recognition. To conclude, the math word problem solvers in other languages than English are still at a very early stage. The datasets used are neither large-scale nor challenging and the proposed techniques are obsolete. This research area has great room for improvement and calls for more efforts to be involved.