ACCENTOR: Adding Chit-Chat to Enhance Task-Oriented Dialogues

Overview

ACCENTOR consists of the human-annotated chit-chat additions to the 23.8K dialogues from Schema Guided Dialogue (SGD) and MultiWOZ 2.1, allowing researchers to study contexutal addition of chit-chat utterances for virtual assistants, to make task-oriented dialogues more engaging and social.

We also provide three new models for ACCENTOR explicitly trained to predict user goals and to generate contextually relevant chit-chat responses.

Automatic and human evaluations show that, compared with the state of-the-art task-oriented baseline, our models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike, while maintaining competitive task performance.

For more details, please refer to this paper.

Data

v1.0/candidates-{sgd,multiwoz}.json: Annotated chit-chat candidates. The format is as follows.

{
 "dialogue 1 / id": [
  [
   dialogue 1 / candidate 1 / turn id,
   dialogue 1 / candidate 1 / position,
   dialogue 1 / candidate 1 / candidate,
   dialogue 1 / candidate 1 / label,
   dialogue 1 / candidate 1 / justification
  ],
  [
   dialogue 1 / candidate 2 / turn id,
   ...
  ],
  ...
 ],
 "dialogue 2 / id": [
  ...
 ],
 ...
}

Folder v1.0/accentor-sgd: The augmented SGD dataset. The format follows the original SGD dataset, with two additional keys (i.e., beginning and end) that store lists of (candidate, label, justification) tuples.
- The folder is generated by v1.0/accentor-sgd.py (with v1.0/candidates-sgd.json and the original SGD dataset as input). Usage: python3 v1.0/accentor-sgd.py --help.
v1.0/accentor-multiwoz-1k.json: 1K augmented MultiWOZ 2.1 dialogues. The format follows the original MultiWOZ dataset, with two additional keys (i.e., beginning and end) that store lists of (candidate, label, justification) tuples.
- The file is generated by v1.0/accentor-multiwoz.py (with v1.0/candidates-multiwoz.json and the original MultiWOZ 2.1 dataset as input). Usage: python3 v1.0/accentor-multiwoz.py --help.

Baseline Models

Preparation

Dependencies: ParlAI (af12799a) and Transformers (2.11.0)
Run the following commands to prepare the data for model training and the off-the-shelf models (i.e., a task-oriented dialogue model and a chit-chat model) for Arranger and Rewriter.

cp -r ./v1.0/accentor-sgd .

python3 gen_delex.py

python3 gen_parlai_data.py

parlai train_model -t fromfile:parlaiformat --fromfile_datapath ./parlai --fromfile-datatype-extension true  -m transformer/generator --init-model zoo:tutorial_transformer_generator/model --dict-file zoo:tutorial_transformer_generator/model.dict --embedding-size 512 --n-layers 8 --ffn-size 2048 --dropout 0.1 --n-heads 16 --learn-positional-embeddings True --n-positions 512 --variant xlm --activation gelu --skip-generation True --fp16 True --text-truncate 512 --label-truncate 128 --dict-tokenizer bpe --dict-lower True -lr 1e-06 --optimizer adamax --lr-scheduler reduceonplateau --gradient-clip 0.1 -veps 0.25 --betas 0.9,0.999 --update-freq 1 --attention-dropout 0.0 --relu-dropout 0.0 --skip-generation True -vp 15 -stim 60 -vme 20000 -bs 16 -vmt ppl -vmm min --save-after-valid True --model-file ./train_90M

parlai interactive -mf ./train_90M < lm.input.dev.cc.txt > lm.output.dev.cc.txt

parlai interactive -mf ./train_90M < lm.input.test.cc.txt > lm.output.test.cc.txt

python3 run_language_modeling.py --output_dir=output_gpt2_10epoch_1e-3_fp16 --model_type=gpt2 --model_name_or_path=gpt2 --do_train --train_data_file=lm.input.train.txt --do_eval  --eval_data_file=lm.input.dev.txt --per_device_train_batch_size 2 --gradient_accumulation_steps 18 --num_train_epochs 10 --learning_rate 1e-3 --fp16 --overwrite_output_dir

python3 run_generation.py --input lm.input.dev.eval.txt --output dev.inference.gpt2_10epoch_1e-3_fp16.json --model_name_or_path ./output_gpt2_10epoch_1e-3_fp16 --eos_token_id 50262

python3 run_generation.py --input lm.input.test.eval.txt --output test.inference.gpt2_10epoch_1e-3_fp16.json --model_name_or_path ./output_gpt2_10epoch_1e-3_fp16 --eos_token_id 50262

SimpleTOD+

Dependency: Transformers (2.11.0)

python3 run_language_modeling.py --output_dir=output_both_gpt2_10epoch_1e-3_fp16 --model_type=gpt2 --model_name_or_path=gpt2 --do_train --train_data_file=lm.input.train.both.txt --do_eval  --eval_data_file=lm.input.dev.both.txt --per_device_train_batch_size 2 --gradient_accumulation_steps 18 --num_train_epochs 10 --learning_rate 1e-3 --fp16 --overwrite_output_dir

python3 run_generation.py --input lm.input.dev.eval.txt --output dev.inference.both_gpt2_10epoch_1e-3_fp16.json --model_name_or_path ./output_both_gpt2_10epoch_1e-3_fp16 --eos_token_id 50262

python3 run_generation.py --input lm.input.test.eval.txt --output test.inference.both_gpt2_10epoch_1e-3_fp16.json --model_name_or_path ./output_both_gpt2_10epoch_1e-3_fp16 --eos_token_id 50262

Arranger

Dependency: Transformers (2.2.0)

python3 gen_arranger_input.py

python3 run_multiple_choice.py --model_type roberta --task_name acc --model_name_or_path roberta-base --do_train --do_eval --do_test --do_lower_case --data_dir . --learning_rate 2e-5 --num_train_epochs 3 --max_seq_length 512 --output_dir acc_arranger_roberta_base_3epoch --per_gpu_eval_batch_size=16 --per_gpu_train_batch_size=1 --gradient_accumulation_steps 24 --overwrite_output --save_steps 10000

python3 gen_arranger_output.py

Rewriter

Dependency: Transformers 2.11.0

python3 gen_rewriter_data.py

python3 run_language_modeling.py --output_dir=output_ff_gpt2_10epoch_1e-3_fp16 --model_type=gpt2 --model_name_or_path=gpt2 --do_train --train_data_file=lm.input.train.ff.txt  --do_eval --eval_data_file=lm.input.dev.ff.txt --per_device_train_batch_size 2 --gradient_accumulation_steps 18 --num_train_epochs 10 --learning_rate 1e-3 --fp16 --overwrite_output_dir

python3 run_generation.py --input lm.input.dev.eval.ff.txt --output dev.inference.ff_gpt2_10epoch_1e-3_fp16.json --model_name_or_path ./output_ff_gpt2_10epoch_1e-3_fp16 --eos_token_id 50262

python3 run_generation.py --input lm.input.test.eval.ff.txt --output test.inference.ff_gpt2_10epoch_1e-3_fp16.json --model_name_or_path ./output_ff_gpt2_10epoch_1e-3_fp16 --eos_token_id 50262

Evaluation

Dependency: the official evaluation script of SGD
Pass the output inference files (i.e., {dev,test}.inference*.json) to gen_predict.py to obtain act-slot F1 and BLEU-4 scores. For example,

python3 gen_predict.py --inference test.inference.both_gpt2_10epoch_1e-3_fp16.json --split test

The above command will also generate a folder (named ./prediction/ by default), which can be passed to the official evaluation script of SGD to obtain the joint goal accuracy and average accuracy. For example,

python3 -m schema_guided_dst.evaluate --dstc8_data_dir ./simpletod/ --prediction_dir ./prediction/test/ --eval_set test --output_metric_file simpletod+_test_result.json

Citations

If you want to publish experimental results with our datasets or use the baseline models, please cite the following article (pdf):

@inproceedings{sun2020adding,
  title={Adding Chit-Chat to Enhance Task-Oriented Dialogues},
  author={Sun, Kai and Moon, Seungwhan and Crook, Paul and Roller, Stephen and Silvert, Becka and Liu, Bing and Wang, Zhiguang and Liu, Honglei and Cho, Eunjoon and Cardie, Claire},
  booktitle={Proceedings of the NAACL-HLT},
  year={2021},
  url={https://arxiv.org/abs/2010.12757}
}

License

ACCENTOR is released under CC-BY-SA-4.0, see LICENSE for details.

Data & Code for ACCENTOR Adding Chit-Chat to Enhance Task-Oriented Dialogues

Related tags

Overview

ACCENTOR: Adding Chit-Chat to Enhance Task-Oriented Dialogues

Overview

Data

Baseline Models

Citations

License

Owner

Facebook Research

It helps user to learn Pick-up lines and share if he has a better one

Veri Setinizi Yolov5 Formatına Dönüştürün

Ensembling Off-the-shelf Models for GAN Training

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

This is a TensorFlow implementation for C2-Rec

This repository contains code, network definitions and pre-trained models for working on remote sensing images using deep learning

Code of Periodic Activation Functions Induce Stationarity

Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

PyTorch implementation of the REMIND method from our ECCV-2020 paper "REMIND Your Neural Network to Prevent Catastrophic Forgetting"

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

Deeper insights into graph convolutional networks for semi-supervised learning

The Deep Learning with Julia book, using Flux.jl.

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

Deep Learning for Time Series Forecasting.

Iowa Project - My second project done at General Assembly, focused on feature engineering and understanding Linear Regression as a concept

Official code for the paper "Self-Supervised Prototypical Transfer Learning for Few-Shot Classification"

Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"