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Autor:
Rücklé, Andreas; Gurevych, Iryna:

Titel:
End-to-end non-factoid question answering with an interactive visualization of neural attention weights

Quelle:
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30 - August 4, 2017 Stroudsburg, PA : Association for Computational Linguistics (2017) , 19-24

URL des Volltextes:
https://aclanthology.info/pdf/P/P17/P17-4004.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Computerlinguistik, Aufmerksamkeit, Vernetzung, Modell, Struktur, Analyse, Visualisierung, Forschung, Frage, Antwort, Benutzeroberfläche


Abstract(original):
Advanced attention mechanisms are an important part of sucessful neural network approaches for non-factoid answer selection because they allow the models to focus on few important segments within rather long answer texts. Analyzing attention mechanisms is thus crucial for understanding strengths and weaknesses of particular models. We present an extensible, highly modular service architecture that enables the transformation of neural network models for non-factoid answer selection into fully featured end-to-end question answering systems. The primary objective of our system is to enable researchers a way to interactively explore and compare attention-based neural networks for answer selection. Our interactive user interface helps researchers to better understand the capabilities of the different approaches and can aid qualitative analyses. The source-code of our system is publicly available. (DIPF/Orig.)


DIPF-Abteilung:
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last modified Nov 11, 2016