ConvFiT: Conversational Fine-Tuning of Pretrained Language Models

Ivan Vulić, Pei-Hao Su, Samuel Coope, Daniela Gerz, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Tsung-Hsien Wen


Abstract
Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind conversationally pretrained (e.g., via response selection) encoders on conversational tasks such as intent detection (ID). In this work, we propose ConvFiT, a simple and efficient two-stage procedure which turns any pretrained LM into a universal conversational encoder (after Stage 1 ConvFiT-ing) and task-specialised sentence encoder (after Stage 2). We demonstrate that 1) full-blown conversational pretraining is not required, and that LMs can be quickly transformed into effective conversational encoders with much smaller amounts of unannotated data; 2) pretrained LMs can be fine-tuned into task-specialised sentence encoders, optimised for the fine-grained semantics of a particular task. Consequently, such specialised sentence encoders allow for treating ID as a simple semantic similarity task based on interpretable nearest neighbours retrieval. We validate the robustness and versatility of the ConvFiT framework with such similarity-based inference on the standard ID evaluation sets: ConvFiT-ed LMs achieve state-of-the-art ID performance across the board, with particular gains in the most challenging, few-shot setups.
Anthology ID:
2021.emnlp-main.88
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1151–1168
Language:
URL:
https://aclanthology.org/2021.emnlp-main.88
DOI:
10.18653/v1/2021.emnlp-main.88
Bibkey:
Cite (ACL):
Ivan Vulić, Pei-Hao Su, Samuel Coope, Daniela Gerz, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, and Tsung-Hsien Wen. 2021. ConvFiT: Conversational Fine-Tuning of Pretrained Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1151–1168, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models (Vulić et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.88.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.88.mp4
Data
ATISCLINC150Reddit Corpus