Rita Sevastjanova


2021

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Is that really a question? Going beyond factoid questions in NLP
Aikaterini-Lida Kalouli | Rebecca Kehlbeck | Rita Sevastjanova | Oliver Deussen | Daniel Keim | Miriam Butt
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer. However, human discourse involves more than that: it contains non-canonical questions deployed to achieve specific communicative goals. In this paper, we investigate this under-studied aspect of NLP by introducing a targeted task, creating an appropriate corpus for the task and providing baseline models of diverse nature. With this, we are also able to generate useful insights on the task and open the way for future research in this direction.

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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.

2020

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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.

2019

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ParHistVis: Visualization of Parallel Multilingual Historical Data
Aikaterini-Lida Kalouli | Rebecca Kehlbeck | Rita Sevastjanova | Katharina Kaiser | Georg A. Kaiser | Miriam Butt
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language. Often, those advantages cannot be fully exploited due to the sparse but high-dimensional nature of such historical data. To tackle this challenge, we introduce ParHistVis: a novel, free, easy-to-use, interactive visualization tool for parallel, multilingual, diachronic and synchronic linguistic data. We illustrate the suitability of the components of the tool based on a use case of word order change in Romance wh-interrogatives.

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lingvis.io - 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.