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Imagine you are in a supermarket. You have two bananas in your basket and want to buy four apples. How many fruits do you have in total? This seemingly straightforward question can be challenging for data-driven language models, even if trained at scale. However, we would expect such generic language models to possess some mathematical abilities in addition to typical linguistic competence. 

Towards this goal, we investigate if a commonly used language model, BERT, possesses such mathematical abilities and, if so, to what degree. For that, we fine-tune BERT on a popular dataset for word math problems, AQuA-RAT, and conduct several tests to understand learned representations better.

Since we teach models trained on natural language to do formal mathematics, we hypothesize that such models would benefit from training on semi-formal steps that explain how math results are derived. To better accommodate such training, we also propose new pretext tasks for learning mathematical rules. We call them (Neighbor) Reasoning Order Prediction (ROP or NROP). With this new model, we achieve significantly better outcomes than data-driven baselines and even on-par with more tailored models.

IJCNLP-AACL 2021

Measuring and Improving BERT&#39;s Mathematical Abilities by Predicting the Order of Reasoning.

math

reasoning

bert

self-supervised

language models

Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning.

technical paper

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation

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Discriminative Reasoning for Document-level Relation Extraction

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Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning.

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Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning.

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Next from IJCNLP-AACL 2021

PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads