A strong baseline for question relevancy ranking
2018pp. 4810–4815
Citations Over TimeTop 13% of 2018 papers
Abstract
The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks -a task that amounts to question relevancy ranking -involve complex pipelines and manual feature engineering. Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google's search engine. We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair. This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.
Related Papers
- → SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering(2017)9 cited
- → JBNU at SemEval-2020 Task 4: BERT and UniLM for Commonsense Validation and Explanation(2020)3 cited
- → What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP(2021)2 cited
- → A strong baseline for question relevancy ranking(2018)