| Challenge: | Neural Module Networks (NMNs) have limited reasoning abilities and lack numerical reasoning capability. |
| Approach: | They propose to integrate the original question in the interpreter and introduce addition and subtraction modules that perform numerical reasoning over numbers. |
| Outcome: | The proposed methods outperform previous state-of-the-art models on a subset of DROP and achieve competitive reasoning performance. |
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Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text (2021.findings-emnlp)
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| Challenge: | Neural Module Networks (NMNs) is an end-to-end differentiable model in the programmer-interpreter paradigm. |
| Approach: | They propose to make the interpreter question-aware and capture the relationship between entities and numbers in both questions and paragraphs. |
| Outcome: | The proposed models outperform the original models on the DROP dataset and are interpertable by nature. |
NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)
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| Challenge: | Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting. |
| Approach: | They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset. |
| Outcome: | The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset. |
Structure Learning for Neural Module Networks (D19-64)
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| Challenge: | Neural Module Networks are a class of neural networks that involve human-specified neural modules . current models only learn the parameters of the modules and/or the order of their execution . |
| Approach: | They propose to learn internal structure and sequence without extra supervisory signals . they use dynamically composable modules which are then assembled into a layout . |
| Outcome: | The proposed model performs comparable to models using hand-designed modules. |
Learning Multi-Step Reasoning by Solving Arithmetic Tasks (2023.acl-short)
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| Challenge: | Recent studies have demonstrated large LMs’ impressive performance in solving math problems, but such ability seems only to emerge from models with abundant parameters. |
| Approach: | They propose to continuously pre-train LMs with the capabilities of multi-step reasoning by continuously pretraining them on a synthetic dataset MsAT. |
| Outcome: | The proposed method improves LMs' multi-step reasoning abilities on four math word problem datasets. |
Complex Numerical Reasoning with Numerical Semantic Pre-training Framework (2025.emnlp-main)
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| Challenge: | Numerical knowledge graphs (NKGs) are not limited to discrete entity-relation knowledge. |
| Approach: | They propose to combine numerical values and entities to solve multi-hop complex reasoning over incomplete knowledge graphs. |
| Outcome: | The proposed approach handles up to 102 types of complex numerical reasoning queries on three public datasets. |
Numerical reasoning in machine reading comprehension tasks: are we there yet? (2021.emnlp-main)
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| Challenge: | Numerical reasoning based machine reading comprehension models have achieved near-human performance on a variety of benchmarks, but are they capable of learning to reason? |
| Approach: | They propose to use a DROP benchmark to measure machine reading comprehension and investigate models that have achieved near-human performance over standard metrics. |
| Outcome: | The DROP benchmark has inspired the design of specialized BERT and embedding the results into a specialized model. |
Multi-Step Inference for Reasoning Over Paragraphs (2020.emnlp-main)
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| Challenge: | Existing models for complex reasoning use symbols or black-box transformers . a compositional model can chain together free-form predicates and logical connectives . |
| Approach: | They propose a compositional model that finds relevant sentences and then chains them together using neural modules. |
| Outcome: | The proposed model improves performance on a recently-introduced dataset. |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
FERMAT: An Alternative to Accuracy for Numerical Reasoning (2023.acl-long)
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| Challenge: | Existing numerical reasoning models are too weak for downstream tasks like fact-checking . FERMAT evaluates models on number understanding, mathematical operations, and training dependency . |
| Approach: | They propose a multi-view evaluation set for numerical reasoning in English that evaluates models on key numerical reasoning aspects instead of reporting a single score on a whole dataset. |
| Outcome: | FERMAT evaluates models on number understanding, mathematical operations, and training dependency. |
Self-training Language Models for Arithmetic Reasoning (2024.findings-emnlp)
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| Challenge: | Recent work improves the reasoning capabilities of language models by scaling training data to more diverse or complex collections, but reaching further improvements becomes exceedingly expensive. |
| Approach: | They propose to use implicit feedback to improve models' reasoning capabilities by training from implicit feedback. |
| Outcome: | The proposed model can reach a correct result in +13.9% and +25.9% more cases than previous models, underlining the importance of actuality of self-training feedback. |