# Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots

arXiv: https://arxiv.org/abs/1805.02333 · HTML: https://mlsanity.com/paper/1805.02333/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

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

- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling](https://mlsanity.com/paper/2102.03551/) (2021)
- [Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task](https://mlsanity.com/paper/2210.17238/) (2022)
- [A comprehensive solution to retrieval-based chatbot construction](https://mlsanity.com/paper/2106.06139/) (2021)
- [WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue](https://mlsanity.com/paper/2108.01487/) (2021)
- [Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues](https://mlsanity.com/paper/2307.06703/) (2023)

## Abstract (arXiv)

We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method.