Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots
This work addresses the challenge of enhancing chatbot responses in customer support, but it is incremental as it applies an existing method to a new task.
The paper tackles the problem of improving answer quality in customer support chatbots by adapting a machine reading comprehension model to re-rank candidate answers, showing improvements in word overlap and semantic similarity for both individual models and model combinations.
Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents. As a result, deep neural models such as sequence-to-sequence, Memory Networks, and the Transformer have become key ingredients of state-of-the-art dialog systems. While those models are able to generate meaningful responses even in unseen situation, they need a lot of training data to build a reliable model. Thus, most real-world systems stuck to traditional approaches based on information retrieval and even hand-crafted rules, due to their robustness and effectiveness, especially for narrow-focused conversations. Here, we present a method that adapts a deep neural architecture from the domain of machine reading comprehension to re-rank the suggested answers from different models using the question as context. We train our model using negative sampling based on question-answer pairs from the Twitter Customer Support Dataset.The experimental results show that our re-ranking framework can improve the performance in terms of word overlap and semantics both for individual models as well as for model combinations.