Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRecall and Learn: A Memory-augmented Solver for Math Word Problems
أذكر وتعلم: حلال مخصص للذاكرة لمشاكل كلمة الرياضيات
291
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه المقالة، نتعامل مع مشكلة كلمة الرياضيات، وهي الإجابة تلقائيا على مشكلة رياضية وفقا لوصفها النصي. على الرغم من أن الطرق الحديثة أظهرت نتائجها الواعدة، فإن معظم هذه الطرق تستند إلى مخطط الجيل القائم على القوالب تؤدي إلى إمكانية تعميم محدودية. تحقيقا لهذه الغاية، نقترح طريقة التعلم التناظرية تشبه الإنسان الرواية في استدعاء وتعلم بطريقة. يتكون إطار عملنا المقترح من وحدات من وحدات الذاكرة والتمثيل والبيان والتفكير، والتي تم تصميمها لإجراء عملية جديدة من خلال الإشارة إلى التدريبات المستفادة في الماضي. على وجه التحديد، بالنظر إلى مشكلة كلمة الرياضيات، يسترجع النموذج لأول مرة أسئلة مماثلة عن طريق وحدة الذاكرة ثم ترميز المشكلة غير المحددة، وكل سؤال استرجاع باستخدام وحدة تمثيل. علاوة على ذلك، فإن حل المشكلة في طريقة التشبيه، وتقترح وحدة تشبيه ووحدة التفكير مع آلية نسخ نموذج العلاقة المتبادلة بين المشكلة وكل سؤال استرجاع. تظهر تجارب واسعة على مجموعة من مجموعات عمليتين معروفتين تفوق خوارزمية لدينا مقارنة بالمقارنة مع المنافسين غير الفنون الآخرين من كل من مقارنة الأداء الإجمالي ودراسات النطاق الصغير.
In this article, we tackle the math word problem, namely, automatically answering a mathematical problem according to its textual description. Although recent methods have demonstrated their promising results, most of these methods are based on template-based generation scheme which results in limited generalization capability. To this end, we propose a novel human-like analogical learning method in a recall and learn manner. Our proposed framework is composed of modules of memory, representation, analogy, and reasoning, which are designed to make a new exercise by referring to the exercises learned in the past. Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module. Moreover, to solve the problem in a way of analogy, an analogy module and a reasoning module with a copy mechanism are proposed to model the interrelationship between the problem and each retrieved question. Extensive experiments on two well-known datasets show the superiority of our proposed algorithm as compared to other state-of-the-art competitors from both overall performance comparison and micro-scope studies.
References used
https://aclanthology.org/
rate research
Read More
Current neural math solvers learn to incorporate commonsense or domain knowledge by utilizing pre-specified constants or formulas. However, as these constants and formulas are mainly human-specified, the generalizability of the solvers is limited. In
this paper, we propose to explicitly retrieve the required knowledge from math problemdatasets. In this way, we can determinedly characterize the required knowledge andimprove the explainability of solvers. Our two algorithms take the problem text andthe solution equations as input. Then, they try to deduce the required commonsense and domain knowledge by integrating information from both parts. We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containi
ng one-unknown arithmetic word problems, such problems are often considered solved'' with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are eit
her mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such wo
rd problems. In a two-step approach, the problem text is first mapped to a formal representation in a declarative language using a sequence-to-sequence model, and then the resulting representation is executed using a probabilistic programming system to provide the answer. Our best performing model incorporates general-domain contextualised word representations that were finetuned using transfer learning on another in-domain dataset. We also apply end-to-end models to this task, which bring out the importance of the two-step approach in obtaining correct solutions to probability problems.
The aim of this paper is to investigate the similarity measurement approach of translation memory (TM) in five representative computer-aided translation (CAT) tools when retrieving inflectional verb-variation sentences in Arabic to English translatio
n. In English, inflectional affixes in verbs include suffixes only; unlike English, verbs in Arabic derive voice, mood, tense, number and person through various inflectional affixes e.g. pre or post a verb root. The research question focuses on establishing whether the TM similarity algorithm measures a combination of the inflectional affixes as a word or as a character intervention when retrieving a segment. If it is dealt with as a character intervention, are the types of intervention penalized equally or differently? This paper experimentally examines, through a black box testing methodology and a test suite instrument, the penalties that TM systems' current algorithms impose when input segments and retrieved TM sources are exactly the same, except for a difference in an inflectional affix. It would be expected that, if TM systems had some linguistic knowledge, the penalty would be very light, which would be useful to translators, since a high-scoring match would be presented near the top of the list of proposals. However, analysis of TM systems' output shows that inflectional affixes are penalized more heavily than expected, and in different ways. They may be treated as an intervention on the whole word, or as a single character change.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
