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Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, and Yi Zhang

Code of our PPTOD paper: Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

News:

• [2022/02/24] PPTOD is accepted to the main conference of ACL 2022!
• [2021/09/29] PPTOD is publicly released!

Introduction:

Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified model that seamlessly supports both task-oriented dialogue understanding and response generation in a plug-and-play fashion. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. Results show that PPTOD creates new state-of-the-art on all evaluated tasks in both full training and low-resource scenarios. Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators.

Main Results:

The following table shows our models performances on end-to-end dialogue modelling (Inform, Success, BLEU, and Combined Score) on MultiWOZ 2.0. It also shows the dialogue state tracking (DST) results on MultiWOZ 2.0 and intent classification accuracy on Banking77.

Citation:

If you find our paper and resources useful, please kindly cite our paper:

@article{su2021multitask,
   author = {Yixuan Su and
             Lei Shu and
             Elman Mansimov and
             Arshit Gupta and
             Deng Cai and
             Yi{-}An Lai and
             Yi Zhang},
   title     = {Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System},
   booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)",
   publisher = "Association for Computational Linguistics",
   year      = {2022},
   url       = {https://arxiv.org/abs/2109.14739}
}

Example Usage:

In the following, we provide an example of how to use PPTOD to address different TOD tasks without fine-tuning on any downstream task! We assume you have downloaded the pptod-small checkpoint and have it in the "./checkpoints/small/" directory (you can find instructions below).

# load the pre-trained PPTOD-small
import torch
from transformers import T5Tokenizer
model_path = r'./checkpoints/small/'
tokenizer = T5Tokenizer.from_pretrained(model_path)
from E2E_TOD.modelling.T5Model import T5Gen_Model
from E2E_TOD.ontology import sos_eos_tokens
special_tokens = sos_eos_tokens
model = T5Gen_Model(model_path, tokenizer, special_tokens, dropout=0.0, 
        add_special_decoder_token=True, is_training=False)
model.eval()

# prepare some pre-defined tokens and task-specific prompts
sos_context_token_id = tokenizer.convert_tokens_to_ids(['<sos_context>'])[0]
eos_context_token_id = tokenizer.convert_tokens_to_ids(['<eos_context>'])[0]
pad_token_id, sos_b_token_id, eos_b_token_id, sos_a_token_id, eos_a_token_id, \
sos_r_token_id, eos_r_token_id, sos_ic_token_id, eos_ic_token_id = \
tokenizer.convert_tokens_to_ids(['<_PAD_>', '<sos_b>', 
'<eos_b>', '<sos_a>', '<eos_a>', '<sos_r>','<eos_r>', '<sos_d>', '<eos_d>'])
bs_prefix_text = 'translate dialogue to belief state:'
bs_prefix_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(bs_prefix_text))
da_prefix_text = 'translate dialogue to dialogue action:'
da_prefix_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(da_prefix_text))
nlg_prefix_text = 'translate dialogue to system response:'
nlg_prefix_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(nlg_prefix_text))
ic_prefix_text = 'translate dialogue to user intent:'
ic_prefix_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(ic_prefix_text))

# an example dialogue context
dialogue_context = "<sos_u> can i reserve a five star place for thursday night at 3:30 for 2 people <eos_u> <sos_r> i'm happy to assist you! what city are you dining in? <eos_r> <sos_u> seattle please. <eos_u>"
context_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(dialogue_context))

# predict belief state 
input_id = bs_prefix_id + [sos_context_token_id] + context_id + [eos_context_token_id]
input_id = torch.LongTensor(input_id).view(1, -1)
x = model.model.generate(input_ids = input_id, decoder_start_token_id = sos_b_token_id,
            pad_token_id = pad_token_id, eos_token_id = eos_b_token_id, max_length = 128)
print (model.tokenized_decode(x[0]))
# the predicted result is
# <sos_b> [restaurant] rating five star date thursday night start time 3:30 number of people 2 city seattle <eos_b>

# predict dialogue act
input_id = da_prefix_id + [sos_context_token_id] + context_id + [eos_context_token_id]
input_id = torch.LongTensor(input_id).view(1, -1)
x = model.model.generate(input_ids = input_id, decoder_start_token_id = sos_a_token_id,
            pad_token_id = pad_token_id, eos_token_id = eos_a_token_id, max_length = 128)
print (model.tokenized_decode(x[0]))
# the predicted result is
# <sos_a> [restaurant] [inform] restaurant name rating [multiple_choice] restaurant name <eos_a>

# predict system response
input_id = nlg_prefix_id + [sos_context_token_id] + context_id + [eos_context_token_id]
input_id = torch.LongTensor(input_id).view(1, -1)
x = model.model.generate(input_ids = input_id, decoder_start_token_id = sos_r_token_id,
            pad_token_id = pad_token_id, eos_token_id = eos_r_token_id, max_length = 128)
print (model.tokenized_decode(x[0]))
# the predicted result is 
# <sos_r> ok, let me find some options for you. <eos_r>

# predict user intent
input_id = ic_prefix_id + [sos_context_token_id] + context_id + [eos_context_token_id]
input_id = torch.LongTensor(input_id).view(1, -1)
x = model.model.generate(input_ids = input_id, decoder_start_token_id = sos_ic_token_id,
            pad_token_id = pad_token_id, eos_token_id = eos_ic_token_id, max_length = 128)
print (model.tokenized_decode(x[0]))
# the predicted result is 
# <sos_d> [book_restaurant] <eos_d>

1. Environment Setup:

pip3 install -r requirements.txt
python -m spacy download en_core_web_sm

2. PPTOD Checkpoints:

You can download checkpoints of PPTOD with different configurations here.

To use PPTOD, you should download the checkpoint you want and unzip it in the ./checkpoints directory.

Alternatively, you can run the following commands to download the PPTOD checkpoints.

(1) Downloading Pre-trained PPTOD-small Checkpoint:

cd checkpoints
chmod +x ./download_pptod_small.sh
./download_pptod_small.sh

(2) Downloading Pre-trained PPTOD-base Checkpoint:

cd checkpoints
chmod +x ./download_pptod_base.sh
./download_pptod_base.sh

(3) Downloading Pre-trained PPTOD-large Checkpoint:

cd checkpoints
chmod +x ./download_pptod_large.sh
./download_pptod_large.sh

3. Data Preparation:

The detailed instruction for preparing the pre-training corpora and the data of downstream TOD tasks are provided in the ./data folder.

4. Dialogue Multi-Task Pre-training:

To pre-train a PPTOD model from scratch, please refer to details provided in ./Pretraining directory.

5. Benchmark TOD Tasks:

(1) End-to-End Dialogue Modelling:

To perform End-to-End Dialogue Modelling using PPTOD, please refer to details provided in ./E2E_TOD directory.

(2) Dialogue State Tracking:

To perform Dialogue State Tracking using PPTOD, please refer to details provided in ./DST directory.

(3) Intent Classification:

To perform Intent Classification using PPTOD, please refer to details provided in ./IC directory.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.