# Towards Automated Customer Support

arXiv: https://arxiv.org/abs/1809.00303 · HTML: https://mlsanity.com/paper/1809.00303/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2310.05280/) (2023)
- [TWEETSUMM -- A Dialog Summarization Dataset for Customer Service](https://mlsanity.com/paper/2111.11894/) (2021)
- [Revealing Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2104.08728/) (2021)
- [Software-Based Dialogue Systems: Survey, Taxonomy and Challenges](https://mlsanity.com/paper/2106.10901/) (2021)
- [Conversational Agents: Theory and Applications](https://mlsanity.com/paper/2202.03164/) (2022)
- [Summary Grounded Conversation Generation](https://mlsanity.com/paper/2106.03337/) (2021)
- [Utilisation of open intent recognition models for customer support intent detection](https://mlsanity.com/paper/2307.16544/) (2023)
- [ImpReSS: Implicit Recommender System for Support Conversations](https://mlsanity.com/paper/2506.14231/) (2025)

## Abstract (arXiv)

Recent years have seen growing interest in conversational agents, such as chatbots, which are a very good fit for automated customer support because the domain in which they need to operate is narrow. This interest was in part inspired by recent advances in neural machine translation, esp. the rise of sequence-to-sequence (seq2seq) and attention-based models such as the Transformer, which have been applied to various other tasks and have opened new research directions in question answering, chatbots, and conversational systems. Still, in many cases, it might be feasible and even preferable to use simple information retrieval techniques. Thus, here we compare three different models:(i) a retrieval model, (ii) a sequence-to-sequence model with attention, and (iii) Transformer. Our experiments with the Twitter Customer Support Dataset, which contains over two million posts from customer support services of twenty major brands, show that the seq2seq model outperforms the other two in terms of semantics and word overlap.