| 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. |
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Teaching Neural Module Networks to Do Arithmetic (2022.coling-1)
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| 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. |
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (D19-3)
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| Challenge: | Deep Neural Networks (DNN) have been widely employed in industry to address various natural language processing tasks. |
| Approach: | They propose an NLP toolkit that encapsulates neural network modules as building blocks to construct various DNN models with complex architecture. |
| Outcome: | The proposed toolkit can build, train, and test various DNN models with complex architecture. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
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. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
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| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
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. |
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models (2021.naacl-main)
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| Challenge: | Existing approaches to decompose complex tasks into simpler ones do not require annotated decompositions. |
| Approach: | They propose a framework for building interpretable systems that learn to solve complex tasks by decomposing existing models into simpler ones solvable by existing models. |
| Outcome: | The proposed framework is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work. |
Priorless Recurrent Networks Learn Curiously (2020.coling-main)
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| Challenge: | a recent study shows domain-general recurrent neural networks reproduce human language behaviours . a lack of a unified concept of number agreement between these processes is a limitation of the model . |
| Approach: | They propose to use domain-general recurrent neural networks without explicit linguistic inductive biases to reproduce human language behaviours. |
| Outcome: | The proposed model can learn number agreement within unnatural sentences, the authors show . they show that the model has an effective understanding of singular versus plural for individual sentences . |
GraphMR: Graph Neural Network for Mathematical Reasoning (2021.emnlp-main)
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| Challenge: | Existing studies have shown the effectiveness of sequence-to-sequence (Seq2Seque) on mathematics solving. |
| Approach: | They propose a graph-to-sequence neural network which can learn hierarchical information of graphs inputs to solve mathematical problems and speculate answers. |
| Outcome: | The proposed neural network outperforms other neural networks in hidden information learning and mathematics resolving. |
Obtaining Faithful Interpretations from Compositional Neural Networks (2020.acl-main)
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Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, Matt Gardner
| Challenge: | Neural module networks (NMNs) are a popular approach for modeling compositionality but prior work implicitly assumed that the structure of the network modules provides a faithful explanation of the model’s reasoning. |
| Approach: | They propose to use auxiliary supervision to train a model with a structured model that can understand the reasoning process and make better choices for module architecture. |
| Outcome: | The proposed models on two datasets show that the proposed models do not provide a faithful explanation of model behaviour. |