Recurrent Networks in AQA Answer Selection
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | Proceedings of the Twelfth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2018 |
| MU Faculty or unit | |
| Citation | |
| Web | https://nlp.fi.muni.cz/raslan/2018/paper12-Sabol_Medved_Horak.pdf |
| Keywords | question answering; answer selection; QA dataset; SQAD; AQA |
| Description | Unlimited, or open domain, question answering system AQA is being developed and tested with the Simple Question Answering Data-base (SQAD) for the Czech language. AQA is optimized for work with morphologically rich languages and makes use of syntactic cues provided by the morphosyntactic analysis. In this paper, we introduce a new answer selection module being developed for the AQA system. The module is based on recurrent neural networks processing the question and answer sentences to derive the most probable answer sentence. We present the details of the module architecture and offer a detailed evaluation of various hyperparameter setups. The module is trained and tested with 8,500 question-answer pairs using the SQAD v2.1 benchmark dataset. |
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