# End-to-end Conversation Modeling Track in DSTC6

arXiv: https://arxiv.org/abs/1706.07440 · HTML: https://mlsanity.com/paper/1706.07440/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2017.

## Built on by (most cited first, 12 of 12)

- [LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https://mlsanity.com/paper/2305.13711/) (2023)
- [A Comprehensive Assessment of Dialog Evaluation Metrics](https://mlsanity.com/paper/2106.03706/) (2021)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [Automatic Evaluation and Moderation of Open-domain Dialogue Systems](https://mlsanity.com/paper/2111.02110/) (2021)
- [MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation](https://mlsanity.com/paper/2112.07194/) (2021)
- [On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2407.03841/) (2024)
- [RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue](https://mlsanity.com/paper/2309.08156/) (2023)
- [Modeling Performance in Open-Domain Dialogue with PARADISE](https://mlsanity.com/paper/2110.11164/) (2021)
- [MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue](https://mlsanity.com/paper/2206.09403/) (2022)
- [Improving Open-Domain Dialogue Evaluation with a Causal Inference Model](https://mlsanity.com/paper/2301.13372/) (2023)
- [Psychological Metrics for Dialog System Evaluation](https://mlsanity.com/paper/2305.14757/) (2023)
- [ComperDial: Commonsense Persona-grounded Dialogue Dataset and Benchmark](https://mlsanity.com/paper/2406.11228/) (2024)

## Abstract (arXiv)

End-to-end training of neural networks is a promising approach to automatic construction of dialog systems using a human-to-human dialog corpus. Recently, Vinyals et al. tested neural conversation models using OpenSubtitles. Lowe et al. released the Ubuntu Dialogue Corpus for researching unstructured multi-turn dialogue systems. Furthermore, the approach has been extended to accomplish task oriented dialogs to provide information properly with natural conversation. For example, Ghazvininejad et al. proposed a knowledge grounded neural conversation model [3], where the research is aiming at combining conversational dialogs with task-oriented knowledge using unstructured data such as Twitter data for conversation and Foursquare data for external knowledge.However, the task is still limited to a restaurant information service, and has not yet been tested with a wide variety of dialog tasks. In addition, it is still unclear how to create intelligent dialog systems that can respond like a human agent. In consideration of these problems, we proposed a challenge track to the 6th dialog system technology challenges (DSTC6) using human-to-human dialog data to mimic human dialog behaviors. The focus of the challenge track is to train end-to-end conversation models from human-to-human conversation and accomplish end-to-end dialog tasks in various situations assuming a customer service, in which a system plays a role of human agent and generates natural and informative sentences in response to user's questions or comments given dialog context.