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DS-TOD: Efficient Domain Specialization for Task Oriented Dialog

Authors: Chia-Chien Hung, Anne Lauscher, Simone Paolo Ponzetto, Goran Glavaš

ACL 2022. Findings: https://aclanthology.org/2022.findings-acl.72/

Introduction

Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, exploit general dialogic corpora (e.g., Reddit) and thus presumably fail to reliably embed domain-specific knowledge useful for concrete downstream TOD domains. In this work, we investigate the effects of domain specialization of pretrained language models (PLMs) for TOD. Within our DS-TOD framework, we first automatically extract salient domain-specific terms, and then use them to construct DomainCC and DomainReddit -- resources that we leverage for domain-specific pretraining, based on (i) masked language modeling (MLM) and (ii) response selection (RS) objectives, respectively. We further propose a resource-efficient and modular domain specialization by means of domain adapters. Our experiments with prominent TOD tasks -- dialog state tracking (DST) and response retrieval (RR) -- encompassing five domains from the MultiWOZ benchmark demonstrate the effectiveness of DS-TOD. Moreover, we show that the light-weight adapter-based specialization (1) performs comparably to full fine-tuning in single domain setups and (2) is particularly suitable for multi-domain specialization, where besides advantageous computational footprint, it can offer better TOD performance.

Overview of DS-TOD framework:

Citation

If you use any source codes, or datasets included in this repo in your work, please cite the following paper:

@inproceedings{hung-etal-2022-ds,
    title = "{DS}-{TOD}: Efficient Domain Specialization for Task-Oriented Dialog",
    author = "Hung, Chia-Chien  and
      Lauscher, Anne  and
      Ponzetto, Simone Paolo and
      Glava{\v{s}}, Goran",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.72",
    pages = "891--904",
}

Pretrained Models

The pre-trained models can be easily loaded using huggingface Transformers or Adapter-Hub adapter-transformers library using the AutoModel function. Following pre-trained versions are supported:

  • TODBERT/TOD-BERT-JNT-V1: TOD-BERT pre-trained using both MLM and RCL objectives
  • bert-base-cased

The scripts for downstream tasks are mainly modified from here, where there might be slight version differences of the packages, which are noted down in the requirements.txt file.

Datasets

Two datasets DomainCC and DomainReddit are created for intermediate training purpose, in order to encode knowledge via the domain-specific corpus. You can simply download the data from DomainCC and DomainReddit. Or you can modify the scripts for your own usage.

Structure

The dialog_datasets in use of our paper are from MultiWOZ-2.1, which we further followed the preprocessing step from here, and split the five domains into different subfiles. The full dialog_datasets can be found under here.

This repository is currently under the following structure:

.
└── DomainReddit
└── DomainCC
└── downstream
    └── models
    └── utils
    └── dialog_datasets
└── specialization
    └── model
└── img
└── README.md