Papers by Mennatallah El-Assady
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems (2022.emnlp-main)
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| Challenge: | We hypothesize that questioning can enhance human performance and assist solvers . |
| Approach: | They propose to use large language models to generate sequential questions for math word problem-solving . they propose to apply these models to a variety of math word problems . |
| Outcome: | The proposed model improves the performance of a math word problem solver by generating more questions than other models. |
A Diachronic Perspective on User Trust in AI under Uncertainty (2023.emnlp-main)
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| Challenge: | Modern NLP systems are rarely calibrated and are often confidently incorrect about their predictions, which violates users’ mental model and erodes their trust. |
| Approach: | They propose to use a mental model to bet on the correctness of an NLP system and to study how trust is rebuilt as a function of time after these events. |
| Outcome: | The proposed model shows that even a few highly inaccurate confidence estimation instances damage users’ trust in the system and performance, which does not easily recover over time. |
Dia-Lingle: A Gamified Interface for Dialectal Data Collection (2025.acl-demo)
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| Challenge: | Dialects suffer from the scarcity of textual resources and are largely spoken rather than written. |
| Approach: | They propose a gamified interface that combines active learning with gamification to enhance the dialect corpus. |
| Outcome: | The proposed interface demonstrates high levels of user satisfaction while requiring minimal effort. |
lingvis.io - A Linguistic Visual Analytics Framework (P19-3)
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Mennatallah El-Assady, Wolfgang Jentner, Fabian Sperrle, Rita Sevastjanova, Annette Hautli-Janisz, Miriam Butt, Daniel Keim
| Challenge: | Using a modular framework, linguistic visual analytics applications can be rapidly prototypized using a web-based framework. |
| Approach: | They propose a modular framework for rapid prototyping of linguistic, web-based, visual analytics applications. |
| Outcome: | The proposed framework supports rapid prototyping of linguistic, web-based, visual analytics applications. |
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges (2025.acl-demo)
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Thilo Spinner, Rita Sevastjanova, Rebecca Kehlbeck, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Andreas Spitz, Mennatallah El-Assady
| Challenge: | generative language models have become popular, but comprehending their outputs remains challenging for NLP practitioners and linguistic experts. |
| Approach: | They propose to use a beam search tree to examine model outputs to provide information on runner-up candidates and their corresponding probabilities to address these challenges. |
| Outcome: | The proposed method validates existing results and offers additional insights. |
SyntaxShap: Syntax-aware Explainability Method for Text Generation (2024.findings-acl)
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| Challenge: | Existing methods for explaining sequence-to-sequence tasks are not suitable for textual data. |
| Approach: | They propose a local, model-agnostic explainability method that takes into account the syntax in text data and extends Shapley values to account for parsing-based syntactic dependencies. |
| Outcome: | The proposed method is compared to state-of-the-art explainability methods for text generation tasks using various metrics including faithfulness, coherency, and semantic alignment of the explanations to the model. |
CafGa: Customizing Feature Attributions to Explain Language Models (2025.emnlp-demos)
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| Challenge: | Feature attribution methods, such as SHAP and LIME, quantify the influence of each input component in a model. |
| Approach: | They propose a tool for generating and evaluating feature attribution explanations at customizable granularities. |
| Outcome: | The proposed tool is compared with two baseline methods: PartitionSHAP and MExGen. |
Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification (2025.emnlp-demos)
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Chenfei Xiong, Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Zhijing Jin, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Mennatallah El-Assady, Elliott Ash
| Challenge: | Social scientists often need to develop codebooks that can be reliable but require significant human effort. |
| Approach: | They propose a mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models. |
| Outcome: | The proposed framework integrates human expertise with automatic annotation guided by large language models. |
Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals (2023.acl-demo)
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| Challenge: | a spurious correlation exists when a feature correlates with the target label while there is no causal relationship between the feature and the label. |
| Approach: | They propose a dashboard that allows users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions. |
| Outcome: | The proposed dashboard enables users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions and make refinements based on the feedback. |
XplaiNLI: Explainable Natural Language Inference through Visual Analytics (2020.coling-demos)
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| Challenge: | Recent research has revealed some heuristics and biases of natural language inference models. |
| Approach: | They propose an interactive visualization interface that computes Natural Language Inference with different methods and provides explanations for the decisions made by each approach. |
| Outcome: | The proposed interface computes Natural Language Inference (NLI) with three different approaches and provides explanations for the decisions made by each approach. |
Explaining Contextualization in Language Models using Visual Analytics (2021.acl-long)
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| Challenge: | Contextualized language models (LMs) have learned highly transferable and task-agnostic properties of language, even to a degree of imitating the classical NLP pipeline. |
| Approach: | They propose to use an existing similarity-based score to measure contextualization and integrate it into a visual analytics technique that combines the model's layers simultaneously and highlighting intra-layer properties and inter-layer differences. |
| Outcome: | The proposed approach combines linguistically-informed insights with scoring and visual analytics to show that contextualization is neither driven by polysemy nor by pure context variation. |