# An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding

arXiv: https://arxiv.org/abs/2210.11729 · HTML: https://mlsanity.com/paper/2210.11729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?](https://mlsanity.com/paper/2005.00628/) (2020)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020)
- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)
- [FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue](https://mlsanity.com/paper/2205.06262/) (2022)

## Abstract (arXiv)

Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency. This study explores the effects of varying quantities of target task training data on sequential transfer learning in the dialog domain. We hypothesize that a model can utilize the information learned from a source task to better learn a target task, thereby reducing the number of target task training samples required. Unintuitively, our data shows that often target task training data size has minimal effect on how sequential transfer learning performs compared to the same model without transfer learning. Our results lead us to believe that this unexpected result could be due to the effects of catastrophic forgetting, motivating further work into methods that prevent such forgetting.