# Sequential Neural Networks for Noetic End-to-End Response Selection

arXiv: https://arxiv.org/abs/2003.02126 · HTML: https://mlsanity.com/paper/2003.02126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Persona-Based Conversational AI: State of the Art and Challenges](https://mlsanity.com/paper/2212.03699/) (2022)
- [A comprehensive solution to retrieval-based chatbot construction](https://mlsanity.com/paper/2106.06139/) (2021)
- [Persona-Coded Poly-Encoder: Persona-Guided Multi-Stream Conversational Sentence Scoring](https://mlsanity.com/paper/2309.16770/) (2023)
- [Projective Methods for Mitigating Gender Bias in Pre-trained Language Models](https://mlsanity.com/paper/2403.18803/) (2024)

## Abstract (arXiv)

The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (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 presents our systems that are ranked top 1 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 top 1 in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, on two large-scale public multi-turn response selection benchmark datasets.