Martin Boyanov


Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics
Martin Boyanov | Preslav Nakov | Alessandro Moschitti | Giovanni Da San Martino | Ivan Koychev
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5% on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5% of the questions when they are similar in style to how questions are asked in the forum, and 47.3% of the questions, when they are more conversational in style.


SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering
Tsvetomila Mihaylova | Pepa Gencheva | Martin Boyanov | Ivana Yovcheva | Todor Mihaylov | Momchil Hardalov | Yasen Kiprov | Daniel Balchev | Ivan Koychev | Preslav Nakov | Ivelina Nikolova | Galia Angelova
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)