@inproceedings{padmakumar-etal-2023-investigating,
title = "Investigating the Representation of Open Domain Dialogue Context for Transformer Models",
author = "Padmakumar, Vishakh and
Hedayatnia, Behnam and
Jin, Di and
Lange, Patrick and
Kim, Seokhwan and
Peng, Nanyun and
Liu, Yang and
Hakkani-Tur, Dilek",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.50",
doi = "10.18653/v1/2023.sigdial-1.50",
pages = "538--547",
abstract = "The bulk of work adapting transformer models to open-domain dialogue represents dialogue context as the concatenated set of turns in natural language. However, it is unclear if this is the best approach. In this work, we investigate this question by means of an empirical controlled experiment varying the dialogue context format from text-only formats (all recent utterances, summaries, selected utterances) as well as variants that are more structurally different (triples, AMR). We compare these formats based on fine-tuned model performance on two downstream tasks{---}knowledge selection and response generation. We find that simply concatenating the utterances works as a strong baseline in most cases, but is outperformed in longer contexts by a hybrid approach of combining a summary of the context with recent utterances. Through empirical analysis, our work highlights the need to examine the format of context representation and offers recommendations on adapting general-purpose language models to dialogue tasks.",
}
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<abstract>The bulk of work adapting transformer models to open-domain dialogue represents dialogue context as the concatenated set of turns in natural language. However, it is unclear if this is the best approach. In this work, we investigate this question by means of an empirical controlled experiment varying the dialogue context format from text-only formats (all recent utterances, summaries, selected utterances) as well as variants that are more structurally different (triples, AMR). We compare these formats based on fine-tuned model performance on two downstream tasks—knowledge selection and response generation. We find that simply concatenating the utterances works as a strong baseline in most cases, but is outperformed in longer contexts by a hybrid approach of combining a summary of the context with recent utterances. Through empirical analysis, our work highlights the need to examine the format of context representation and offers recommendations on adapting general-purpose language models to dialogue tasks.</abstract>
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%0 Conference Proceedings
%T Investigating the Representation of Open Domain Dialogue Context for Transformer Models
%A Padmakumar, Vishakh
%A Hedayatnia, Behnam
%A Jin, Di
%A Lange, Patrick
%A Kim, Seokhwan
%A Peng, Nanyun
%A Liu, Yang
%A Hakkani-Tur, Dilek
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F padmakumar-etal-2023-investigating
%X The bulk of work adapting transformer models to open-domain dialogue represents dialogue context as the concatenated set of turns in natural language. However, it is unclear if this is the best approach. In this work, we investigate this question by means of an empirical controlled experiment varying the dialogue context format from text-only formats (all recent utterances, summaries, selected utterances) as well as variants that are more structurally different (triples, AMR). We compare these formats based on fine-tuned model performance on two downstream tasks—knowledge selection and response generation. We find that simply concatenating the utterances works as a strong baseline in most cases, but is outperformed in longer contexts by a hybrid approach of combining a summary of the context with recent utterances. Through empirical analysis, our work highlights the need to examine the format of context representation and offers recommendations on adapting general-purpose language models to dialogue tasks.
%R 10.18653/v1/2023.sigdial-1.50
%U https://aclanthology.org/2023.sigdial-1.50
%U https://doi.org/10.18653/v1/2023.sigdial-1.50
%P 538-547
Markdown (Informal)
[Investigating the Representation of Open Domain Dialogue Context for Transformer Models](https://aclanthology.org/2023.sigdial-1.50) (Padmakumar et al., SIGDIAL 2023)
ACL
- Vishakh Padmakumar, Behnam Hedayatnia, Di Jin, Patrick Lange, Seokhwan Kim, Nanyun Peng, Yang Liu, and Dilek Hakkani-Tur. 2023. Investigating the Representation of Open Domain Dialogue Context for Transformer Models. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 538–547, Prague, Czechia. Association for Computational Linguistics.