Kalouli, Aikaterini-Lida

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Kalouli
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Aikaterini-Lida
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XplaiNLI : Explainable Natural Language Inference through Visual Analytics

2020, Kalouli, Aikaterini-Lida, Sevastjanova, Rita, de Paiva, Valeria, Crouch, Richard, El-Assady, Mennatallah

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.

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Mixed-Initiative Active Learning for Generating Linguistic Insights in Question Classification

2018, Sevastjanova, Rita, El-Assady, Mennatallah, Hautli-Janisz, Annette, Kalouli, Aikaterini-Lida, Kehlbeck, Rebecca, Deussen, Oliver, Keim, Daniel A., Butt, Miriam

We propose a mixed-initiative active learning system to tackle the challenge of building descriptive models for under-studied linguistic phenomena. Our particular use case is the linguistic analysis of question types, in particular in understanding what characterizes information-seeking vs. non-information-seeking questions (i.e., whether the speaker wants to elicit an answer from the hearer or not) and how automated methods can assist with the linguistic analysis. Our approach is motivated by the need for an effective and efficient human-in-the-loop process in natural language processing that relies on example-based learning and provides immediate feedback to the user. In addition to the concrete implementation of a question classification system, we describe general paradigms of explainable mixed-initiative learning, allowing for the user to access the patterns identified automatically by the system, rather than being confronted by a machine learning black box. Our user study demonstrates the capability of our system in providing deep linguistic insight into this particular analysis problem. The results of our evaluation are competitive with the current state-of-the-art.