# Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots

## TL;DR

This paper introduces a machine reading comprehension-based re-ranking method for customer support chatbots, improving answer relevance by leveraging deep neural models trained on Twitter customer support data.

## Contribution

It adapts machine reading comprehension techniques for answer re-ranking in customer support chatbots, enhancing response quality over traditional methods.

## Key findings

- Improved answer relevance in chatbot responses.
- Enhanced semantic matching between questions and answers.
- Effective re-ranking using neural models trained on real-world data.

## Abstract

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.