EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers (2022.acl-long)
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| Challenge: | Existing neural approaches to solve algebraic word problems have a plausible answer, but this belief has less been verified due to Q. |
| Approach: | They propose a neural model EPT-X which utilizes natural language explanations to solve an algebraic word problem. |
| Outcome: | The proposed model achieves an average performance of 69.59% on a PEN dataset and produces explanations with quality comparable to human output. |
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| Challenge: | Existing models that generate solution equations using ‘Op (operator/operand) tokens suffered expression fragmentation and operand-context separation. |
| Approach: | They propose a pure neural model, Expression-Pointer Transformer, which uses (1) ‘Expression’ token and (2) operand-context pointers when generating solution equations. |
| Outcome: | The proposed model achieves comparable performance accuracy to state-of-the-art models and achieves better performance than existing models by at most 40%. |
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems (N19-1)
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| Challenge: | Existing methods to solve math word problems require accurate natural language understanding to bridge texts and math expressions. |
| Approach: | They propose a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. |
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Ecco: An Open Source Library for the Explainability of Transformer Language Models (2021.acl-demo)
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| Challenge: | Existing models that use the Transformer architecture are lag behind our ability to scale them. |
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Noun-MWP: Math Word Problems Meet Noun Answers (2022.coling-1)
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| Challenge: | Existing MWP solvers can handle Noun-MWPs, but they are not as efficient as other models. |
| Approach: | They propose a method to empower existing MWP solvers to handle Noun-MWPs. |
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MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)
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| Challenge: | Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations. |
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InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021.eacl-demos)
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Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
| Challenge: | Using Transformer-based models for NLU/NLP tasks is a growing interest . but there are many open questions regarding the behavior of these models . |
| Approach: | They present an interactive visualization tool for interpreting Transformer-based models. |
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Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems (2022.aacl-short)
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| Challenge: | Existing methods to solve Math Word Problems rely on human annotation . empirical results suggest that our method universally improves the performance on single-unknown and multiple-un unknown benchmarks. |
| Approach: | They propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. |
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Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. |
| Approach: | They propose a method called controlled generation with Prompt Insertion that uses Large Language Models to explain the reasons for corrections in natural language. |
| Outcome: | The proposed method can explain the reasons for corrections in natural language by guiding the LLMs to generate explanations for all correction points. |