Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TABMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TABMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multi-choice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TABMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TABMWP. To mitigate this, we further propose a novel approach, PROMPTPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples.
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Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog
Product catalogs, conceptually in the form of
text-rich tables, are self-reported by individual
retailers and thus inevitably contain noisy
facts. Verifying such textual attributes in product
catalogs is essential to improve their reliability.
However, popular methods for processing
free-text content, such as pre-trained
language models, are not particularly effective
on structured tabular data since they are typically
trained on free-form natural language
texts. In this paper, we present Tab-Cleaner, a
model designed to handle error detection over
text-rich tabular data following a pre-training
/ fine-tuning paradigm. We train Tab-Cleaner
on a real-world Amazon Product Catalog table
w.r.t millions of products and show improvements
over state-of-the-art methods by 16% on
PR AUC over attribute applicability classification
task and by 11% on PR AUC over attribute
value validation task.
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- NSF-PAR ID:
- 10464429
- Date Published:
- Journal Name:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
- Volume:
- 5
- Page Range / eLocation ID:
- 172 to 185
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2023.acl-industry.18
