mlsanityData to 7 Oct 2026

ConveRT for FAQ Answering

arXiv 2108.00719 ↗First posted 2 Aug 2021cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

Knowledgeable FAQ chatbots are a valuable resource to any organization. While powerful and efficient retrieval-based models exist for English, it is rarely the case for other languages for which the same amount of training data is not available. In this paper, we propose a novel pre-training procedure to adapt ConveRT, an English conversational retriever model, to other languages with less training data available. We apply it for the first time to the task of Dutch FAQ answering related to the COVID-19 vaccine. We show it performs better than an open-source alternative in both a low-data regime and a high-data regime.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 2 Aug 2021.

Builds on

11 of 30 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  4. 2021Internet-Augmented Dialogue Generationcited by 217
  5. 2019BERTje: A Dutch BERT Modelcited by 109
  6. 2019ConveRT: Efficient and Accurate Conversational Representations from Transformerscited by 95
  7. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  8. 2017Rasa: Open Source Language Understanding and Dialogue Managementcited by 67
  9. 2019Training Neural Response Selection for Task-Oriented Dialogue Systemscited by 48
  10. 2019FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevancecited by 24
  11. 2020Probing Task-Oriented Dialogue Representation from Language Modelscited by 15

Built on by

Cited by year

Citing papers per year: 2021: 1.

1 paper.