@inproceedings{albalak-etal-2022-feta,
title = "{FETA}: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue",
author = "Albalak, Alon and
Tuan, Yi-Lin and
Jandaghi, Pegah and
Pryor, Connor and
Yoffe, Luke and
Ramachandran, Deepak and
Getoor, Lise and
Pujara, Jay and
Wang, William Yang",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.751/",
doi = "10.18653/v1/2022.emnlp-main.751",
pages = "10936--10953",
abstract = "Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA: a benchmark for FEw-sample TAsk transfer in open-domain dialogue.FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work.We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer.In addition to task transfer, FETA can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as for learning settings such as continual and multitask learning."
}
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<abstract>Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA: a benchmark for FEw-sample TAsk transfer in open-domain dialogue.FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work.We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer.In addition to task transfer, FETA can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as for learning settings such as continual and multitask learning.</abstract>
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%0 Conference Proceedings
%T FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
%A Albalak, Alon
%A Tuan, Yi-Lin
%A Jandaghi, Pegah
%A Pryor, Connor
%A Yoffe, Luke
%A Ramachandran, Deepak
%A Getoor, Lise
%A Pujara, Jay
%A Wang, William Yang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F albalak-etal-2022-feta
%X Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA: a benchmark for FEw-sample TAsk transfer in open-domain dialogue.FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work.We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer.In addition to task transfer, FETA can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as for learning settings such as continual and multitask learning.
%R 10.18653/v1/2022.emnlp-main.751
%U https://aclanthology.org/2022.emnlp-main.751/
%U https://doi.org/10.18653/v1/2022.emnlp-main.751
%P 10936-10953
Markdown (Informal)
[FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue](https://aclanthology.org/2022.emnlp-main.751/) (Albalak et al., EMNLP 2022)
ACL
- Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, and William Yang Wang. 2022. FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10936–10953, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.