Mennatallah El-Assady


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Explaining Contextualization in Language Models using Visual Analytics
Rita Sevastjanova | Aikaterini-Lida Kalouli | Christin Beck | Hanna Schäfer | Mennatallah El-Assady
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn. In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights. In particular, we utilize scoring and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model’s layers simultaneously and highlighting intra-layer properties and inter-layer differences. We show that contextualization is neither driven by polysemy nor by pure context variation. We also provide insights on why BERT fails to model words in the middle of the functionality continuum.


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Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias
Christin Beck | Hannah Booth | Mennatallah El-Assady | Miriam Butt
Proceedings of the 14th Linguistic Annotation Workshop

The development of linguistic corpora is fraught with various problems of annotation and representation. These constitute a very real challenge for the development and use of annotated corpora, but as yet not much literature exists on how to address the underlying problems. In this paper, we identify and discuss five sources of representation problems, which are independent though interrelated: ambiguity, variation, uncertainty, error and bias. We outline and characterize these sources, discussing how their improper treatment can have stark consequences for research outcomes. Finally, we discuss how an adequate treatment can inform corpus-related linguistic research, both computational and theoretical, improving the reliability of research results and NLP models, as well as informing the more general reproducibility issue.

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XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Aikaterini-Lida Kalouli | Rita Sevastjanova | Valeria de Paiva | Richard Crouch | Mennatallah El-Assady
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions. To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.


pdf bib - A Linguistic Visual Analytics Framework
Mennatallah El-Assady | Wolfgang Jentner | Fabian Sperrle | Rita Sevastjanova | Annette Hautli-Janisz | Miriam Butt | Daniel Keim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present a modular framework for the rapid-prototyping of linguistic, web-based, visual analytics applications. Our framework gives developers access to a rich set of machine learning and natural language processing steps, through encapsulating them into micro-services and combining them into a computational pipeline. This processing pipeline is auto-configured based on the requirements of the visualization front-end, making the linguistic processing and visualization design, detached independent development tasks. This paper describes the constellation and modality of our framework, which continues to support the efficient development of various human-in-the-loop, linguistic visual analytics research techniques and applications.


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Interactive Visual Analysis of Transcribed Multi-Party Discourse
Mennatallah El-Assady | Annette Hautli-Janisz | Valentin Gold | Miriam Butt | Katharina Holzinger | Daniel Keim
Proceedings of ACL 2017, System Demonstrations