Laying Anchors: Semantically Priming Numerals in Language Modeling
This addresses a specific limitation in NLP pipelines for tasks requiring numeric understanding, though it appears incremental as it builds on existing language modeling approaches.
The paper tackles the problem of pre-trained language models' poor numeric comprehension by introducing strategies to semantically prime numerals using corpus-specific anchors, resulting in mathematically grounded representations. It demonstrates significant improvements on numeracy tasks for numerals ranging from 1 to 10 billion, a broader range than previous studies.
Off-the-shelf pre-trained language models have become the de facto standard in NLP pipelines for a multitude of downstream tasks. However, the inability of these models to properly encode numerals limits their performance on tasks requiring numeric comprehension. We introduce strategies to semantically prime numerals in any corpus by generating anchors governed by the distribution of numerals in said corpus, thereby enabling mathematically grounded representations of these numeral tokens. We establish the superiority of our proposed techniques through evaluation on a range of numeracy tasks for both in-domain (seen) and out-domain (unseen) numerals. Further, we expand our empirical evaluations to numerals ranging from 1 to 10 billion, a significantly broader range compared to previous studies of the same nature, and we demonstrate significant improvements in the mathematical grounding of our learned embeddings.