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Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations.…

Computation and Language · Computer Science 2018-10-12 Elior Sulem , Omri Abend , Ari Rappoport

Finding model parameters from data is an essential task in science and engineering, from weather and climate forecasts to plasma control. Previous works have employed neural networks to greatly accelerate finding solutions to inverse…

Machine Learning · Computer Science 2024-08-16 Philipp Holl , Nils Thuerey

The nature of abstract reasoning is a matter of debate. Modern artificial neural network (ANN) models, like large language models, demonstrate impressive success when tested on abstract reasoning problems. However, it has been argued that…

Artificial Intelligence · Computer Science 2024-11-11 Tomer Barak , Yonatan Loewenstein

Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We…

Computation and Language · Computer Science 2017-07-18 Dimitrios Alikaniotis , Helen Yannakoudakis , Marek Rei

Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn…

Computation and Language · Computer Science 2016-06-02 Shamil Chollampatt , Kaveh Taghipour , Hwee Tou Ng

Neural networks for computer vision extract uninterpretable features despite achieving high accuracy on benchmarks. In contrast, humans can explain their predictions using succinct and intuitive descriptions. To incorporate explainability…

Computer Vision and Pattern Recognition · Computer Science 2023-07-04 Khalid Saifullah , Yuxin Wen , Jonas Geiping , Micah Goldblum , Tom Goldstein

The purpose of this article is to review the achievements made in the last few years towards the understanding of the reasons behind the success and subtleties of neural network-based machine learning. In the tradition of good old applied…

Machine Learning · Computer Science 2020-12-09 Weinan E , Chao Ma , Stephan Wojtowytsch , Lei Wu

Computers have already changed the way that humans do mathematics: they enable us to compute efficiently. But will they soon be helping us to reason? And will they one day start reasoning themselves? We give an overview of recent…

Artificial Intelligence · Computer Science 2025-02-13 Kevin Buzzard

Large Language Models (LLMs) have shown remarkable success on a wide range of math and reasoning benchmarks. However, we observe that they often struggle when faced with unreasonable math problems. Instead of recognizing these issues,…

Computation and Language · Computer Science 2025-06-03 Jingyuan Ma , Damai Dai , Zihang Yuan , Rui li , Weilin Luo , Bin Wang , Qun Liu , Lei Sha , Zhifang Sui

Language models have demonstrated remarkable performance in solving reasoning tasks; however, even the strongest models still occasionally make reasoning mistakes. Recently, there has been active research aimed at improving reasoning…

Computation and Language · Computer Science 2024-08-30 Tian Ye , Zicheng Xu , Yuanzhi Li , Zeyuan Allen-Zhu

In this paper, we revisit the solving bias when evaluating models on current Math Word Problem (MWP) benchmarks. However, current solvers exist solving bias which consists of data bias and learning bias due to biased dataset and improper…

Artificial Intelligence · Computer Science 2022-10-10 Zhicheng Yang , Jinghui Qin , Jiaqi Chen , Xiaodan Liang

Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology…

Computation and Language · Computer Science 2020-11-10 Natalia Vanetik , Marina Litvak , Sergey Shevchuk , Lior Reznik

Large language models (LLMs) are known to struggle with complicated reasoning tasks such as math word problems (MWPs). In this paper, we present how analogy from similarly structured questions can improve LLMs' problem-solving capabilities…

Computation and Language · Computer Science 2024-11-26 Xiaocong Yang , Jiacheng Lin , Ziqi Wang , Chengxiang Zhai

Neural networks are among the most accurate supervised learning methods in use today. However, their opacity makes them difficult to trust in critical applications, especially when conditions in training may differ from those in practice.…

Machine Learning · Computer Science 2018-10-03 Andrew Slavin Ross

Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods. However, while mathematics is a precise and accurate science, it is usually expressed by less accurate and…

Digital Libraries · Computer Science 2020-12-22 André Greiner-Petter , Terry Ruas , Moritz Schubotz , Akiko Aizawa , William Grosky , Bela Gipp

Large language models (LLMs) have revolutionized NLP by solving downstream tasks with little to no labeled data. Despite their versatile abilities, the larger question of their ability to reason remains ill-understood. This paper addresses…

Computation and Language · Computer Science 2023-08-04 Vedant Gaur , Nikunj Saunshi

The vocabulary mismatch problem is a long-standing problem in information retrieval. Semantic matching holds the promise of solving the problem. Recent advances in language technology have given rise to unsupervised neural models for…

Information Retrieval · Computer Science 2016-11-11 Kezban Dilek Onal , Ismail Sengor Altingovde , Pinar Karagoz , Maarten de Rijke

We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles. Our system works by generating answer candidates for each crossword clue using neural question answering models and then…

Computation and Language · Computer Science 2022-07-05 Eric Wallace , Nicholas Tomlin , Albert Xu , Kevin Yang , Eshaan Pathak , Matthew Ginsberg , Dan Klein

We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challenges, current datasets in this domain have been either…

Computation and Language · Computer Science 2019-06-03 Aida Amini , Saadia Gabriel , Peter Lin , Rik Koncel-Kedziorski , Yejin Choi , Hannaneh Hajishirzi

This paper is devoted to the prediction of solutions to a stochastic discrete optimization problem. Through an application, we illustrate how we can use a state-of-the-art neural machine translation (NMT) algorithm to predict the solutions…

Machine Learning · Computer Science 2019-10-21 Emma Frejinger , Eric Larsen