# Sequential Attention-based Network for Noetic End-to-End Response Selection

arXiv: https://arxiv.org/abs/1901.02609 · HTML: https://mlsanity.com/paper/1901.02609/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Domain-matched Pre-training Tasks for Dense Retrieval](https://mlsanity.com/paper/2107.13602/) (2021)
- [Conversational Query Rewriting with Self-supervised Learning](https://mlsanity.com/paper/2102.04708/) (2021)
- [Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2306.04357/) (2023)
- [CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models](https://mlsanity.com/paper/2205.10558/) (2022)
- [Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing](https://mlsanity.com/paper/2211.02899/) (2022)
- [Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems](https://mlsanity.com/paper/2507.05940/) (2025)

## Abstract (arXiv)

The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper describes our systems that are ranked the top on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous state-of-the-art models use hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among different turns' utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multi-turn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. In addition to ranking the top in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, and achieves new state-of-the-art performances on two large-scale public multi-turn response selection benchmark datasets.