Neural Machine Translation for Mathematical Formulae

• URL: http://arxiv.org/abs/2305.16433v1
• Date: Thu, 25 May 2023 19:15:06 GMT
• Title: Neural Machine Translation for Mathematical Formulae
• Authors: Felix Petersen, Moritz Schubotz, Andre Greiner-Petter, Bela Gipp
• Abstract summary: We tackle the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. We find that convolutional sequence-to-sequence networks achieve 95.1% and 90.7% exact matches, respectively.
• Score: 8.608288231153304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. Compared to neural machine translation on natural language, mathematical formulae have a much smaller vocabulary and much longer sequences of symbols, while their translation requires extreme precision to satisfy mathematical information needs. In this work, we perform the tasks of translating from LaTeX to Mathematica as well as from LaTeX to semantic LaTeX. While recurrent, recursive, and transformer networks struggle with preserving all contained information, we find that convolutional sequence-to-sequence networks achieve 95.1% and 90.7% exact matches, respectively.

Related papers

• MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into $LaTeX$ Formulas for Improved Readability [10.757551947236879]
  We introduce MathBridge, the first extensive dataset for translating mathematical spoken sentences into formulas. MathBridge significantly enhances the capabilities of pretrained language models for converting to formulas from mathematical spoken sentences.
  arXiv  Detail & Related papers  (2024-08-07T18:07:15Z)
• Lean Workbook: A large-scale Lean problem set formalized from natural language math problems [50.22847430754973]
  Large language models are not good at math theorem proving using formal languages like Lean. A significant challenge in this area is the scarcity of training data available in these formal languages. We propose a novel pipeline that iteratively generates and filters synthetic data to translate natural language mathematical problems into Lean 4 statements.
  arXiv  Detail & Related papers  (2024-06-06T08:25:43Z)
• Code-Switching with Word Senses for Pretraining in Neural Machine Translation [107.23743153715799]
  We introduce Word Sense Pretraining for Neural Machine Translation (WSP-NMT) WSP-NMT is an end-to-end approach for pretraining multilingual NMT models leveraging word sense-specific information from Knowledge Bases. Our experiments show significant improvements in overall translation quality.
  arXiv  Detail & Related papers  (2023-10-21T16:13:01Z)
• Tree-Based Representation and Generation of Natural and Mathematical Language [77.34726150561087]
  Mathematical language in scientific communications and educational scenarios is important yet relatively understudied. Recent works on mathematical language focus either on representing stand-alone mathematical expressions, or mathematical reasoning in pre-trained natural language models. We propose a series of modifications to existing language models to jointly represent and generate text and math.
  arXiv  Detail & Related papers  (2023-02-15T22:38:34Z)
• JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding [74.12405417718054]
  This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM) Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
  arXiv  Detail & Related papers  (2022-06-13T17:03:52Z)
• Pretrained Language Models are Symbolic Mathematics Solvers too! [1.9240537487954366]
  Large-scale language models such as transformers are universal and surprisingly can be trained as a sequence-to-sequence task to solve complex equations. We present a sample efficient way of solving the symbolic tasks by first pretraining the transformer model with language translation and then fine-tuning the pretrained transformer model to solve the downstream task of symbolic mathematics.
  arXiv  Detail & Related papers  (2021-10-07T14:37:06Z)
• Zero-Shot Cross-lingual Semantic Parsing [56.95036511882921]
  We study cross-lingual semantic parsing as a zero-shot problem without parallel data for 7 test languages. We propose a multi-task encoder-decoder model to transfer parsing knowledge to additional languages using only English-Logical form paired data. Our system frames zero-shot parsing as a latent-space alignment problem and finds that pre-trained models can be improved to generate logical forms with minimal cross-lingual transfer penalty.
  arXiv  Detail & Related papers  (2021-04-15T16:08:43Z)
• Distilling Wikipedia mathematical knowledge into neural network models [4.874780144224057]
  We introduce a pipeline for distilling mathematical expressions embedded in Wikipedia into symbolic encodings to be used in downstream machine learning tasks. We demonstrate that a $textitmathematical$ $textitlanguage$ $textitmodel$ trained on this "corpus" of expressions can be used as a prior to improve the performance of neural-guided search for the task of symbolic regression.
  arXiv  Detail & Related papers  (2021-04-13T04:16:50Z)
• Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units [86.9207811656179]
  We show that memory-augmented neural networks (NNs) can achieve higher-order, memory-augmented extrapolation, stable performance, and faster convergence. Our models achieve a 1.53% average improvement over current state-of-the-art methods in equation verification and achieve a 2.22% Top-1 average accuracy and 2.96% Top-5 average accuracy for equation completion.
  arXiv  Detail & Related papers  (2021-04-07T03:50:11Z)
• Learning to Match Mathematical Statements with Proofs [37.38969121408295]
  The task is designed to improve the processing of research-level mathematical texts. We release a dataset for the task, consisting of over 180k statement-proof pairs. We show that considering the assignment problem globally and using weighted bipartite matching algorithms helps a lot in tackling the task.
  arXiv  Detail & Related papers  (2021-02-03T15:38:54Z)
• Machine Translation of Mathematical Text [0.0]
  We have implemented a machine translation system, the PolyMath Translator, for documents containing mathematical text. The current implementation translates English to French, attaining a BLEU score of 53.5 on a held-out test corpus of mathematical sentences. It produces documents that can be compiled to PDF without further editing.
  arXiv  Detail & Related papers  (2020-10-11T11:59:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.