Official repository for "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems"

Related tags

Deep Learning abcd
Overview

Action-Based Conversations Dataset (ABCD)

This respository contains the code and data for ABCD (Chen et al., 2021)

Introduction

Whereas existing goal-oriented dialogue datasets focus mainly on identifying user intents, customer interactions in reality often involve agents following multi-step procedures derived from explicitly-defined guidelines. For example, in a online shopping scenario, a customer might request a refund for a past purchase. However, before honoring such a request, the agent should check the company policies to see if a refund is warranted. It is very likely that the agent will need to verify the customer's identity and check that the purchase was made within a reasonable timeframe.

To study dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), where an agent's actions must be balanced between the desires expressed by the customer and the constraints set by company policies. The dataset contains over 10K human-to-human dialogues with 55 distinct user intents requiring unique sequences of actions to achieve task success. We also design a new technique called Expert Live Chat for collecting data when there are two unequal users engaging in real-time conversation. Please see the paper for more details.

Paper link: https://arxiv.org/abs/2104.00783

Blog link: https://www.asapp.com/blog/action-based-conversations-dataset/

Agent Dashboard

Customer Site

Usage

All code is run by executing the corresponding command within the shell script run.sh, which will kick off the data preparation and training within main.py. To use, first unzip the file found in data/abcd_v1.1.json.gz using the gunzip command (or similar). Then comment or uncomment the appropriate lines in the shell script to get desired behavior. Finally, enter sh run.sh into the command line to get started. Use the --help option of argparse for flag details or read through the file located within utils/arguments.py.

Preparation

Raw data will be loaded from the data folder and prepared into features that are placed into Datasets. If this has already occured, then the system will instead read in the prepared features from cache.

If running CDS for the first time, uncomment out the code within the run script to execute embed.py which will prepare the utterances for ranking.

Training

To specify the task for training, simply use the --task option with either ast or cds, for Action State Tracking and Cascading Dialogue Success respectively. Options for different model types are bert, albert and roberta. Loading scripts can be tuned to offer various other behaviors.

Evaluation

Activate evaluation using the --do-eval flag. By default, run.sh will perform cascading evaluation. To include ablations, add the appropriate options of --use-intent or --use-kb.

Data

The preprocessed data is found in abcd_v1.1.json which is a dictionary with keys of train, dev and test. Each split is a list of conversations, where each conversation is a dict containing:

  • convo_id: a unique conversation identifier
  • scenario: the ground truth scenario used to generate the prompt
  • original: the raw conversation of speaker and utterances as a list of tuples
  • delexed: the delexicalized conversation used for training and evaluation, see below for details

We provide the delexed version so new models performing the same tasks have comparable pre-processing. The original data is also provided in case you want to use the utterances for some other purpose.

For a quick preview, a small sample of chats is provided to help get started. Concretely, abcd_sample.json is a list containing three random conversations from the training set.

Scenario

Each scene dict contains details about the customer setup along with the underlying flow and subflow information an agent should use to address the customer concern. The components are:

  • Personal: personal data related to the (fictional) customer including account_id, customer name, membership level, phone number, etc.
  • Order: order info related to what the customer purchased or would like to purchase. Includes address, num_products, order_id, product names, and image info
  • Product: product details if applicable, includes brand name, product type and dollar amount
  • Flow and Subflow: these represent the ground truth user intent. They are used to generate the prompt, but are not shown directly the customer. The job of the agent is to infer this (latent) intent and then match against the Agent Guidelines to resolve the customer issue.

Guidelines

The agent guidelines are offered in their original form within Agent Guidelines for ABCD. This has been transformed into a formatted document for parsing by a model within data/guidelines.json. The intents with their button actions about found within kb.json. Lastly, the breakdown of all flows, subflows, and actions are found within ontology.json.

Conversation

Each conversation is made up of a list of turns. Each turn is a dict with five parts:

  • Speaker: either "agent", "customer" or "action"
  • Text: the utterance of the agent/customer or the system generated response of the action
  • Turn_Count: integer representing the turn number, starting from 1
  • Targets : list of five items representing the subtask labels
    • Intent Classification (text) - 55 subflow options
    • Nextstep Selection (text) - take_action, retrieve_utterance or end_conversation; 3 options
    • Action Prediction (text) - the button clicked by the agent; 30 options
    • Value Filling (list) - the slot value(s) associated with the action above; 125 options
    • Utterance Ranking (int) - target position within list of candidates; 100 options
  • Candidates: list of utterance ids representing the pool of 100 candidates to choose from when ranking. The surface form text can be found in utterances.json where the utt_id is the index. Only applicable when the current turn is a "retrieve_utterance" step.

In contrast to the original conversation, the delexicalized version will replace certain segments of text with special tokens. For example, an utterance might say "My Account ID is 9KFY4AOHGQ". This will be changed into "my account id is <account_id>".

Contact

Please email [email protected] for questions or feedback.

Citation

@inproceedings{chen2021abcd,
    title = "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems",
    author = "Chen, Derek and
        Chen, Howard and
        Yang, Yi and
        Lin, Alex and
        Yu, Zhou",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for 
    	Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.239",
    pages = "3002--3017"
}
You might also like...
The official repository for BaMBNet

BaMBNet-Pytorch Paper

CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

selfcontact This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] It includes the main function

CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

SMPLify-XMC This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] License Software Copyright Lic

This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.
Official repository for the paper "Going Beyond Linear Transformers with Recurrent Fast Weight Programmers"

Recurrent Fast Weight Programmers This is the official repository containing the code we used to produce the experimental results reported in the pape

Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"

Easy-To-Hard The official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks". Gett

Official repository for the CVPR 2021 paper "Learning Feature Aggregation for Deep 3D Morphable Models"

Deep3DMM Official repository for the CVPR 2021 paper Learning Feature Aggregation for Deep 3D Morphable Models. Requirements This code is tested on Py

Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.
Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.

MidiBERT-Piano Authors: Yi-Hui (Sophia) Chou, I-Chun (Bronwin) Chen Introduction This is the official repository for the paper, MidiBERT-Piano: Large-

Official repository of my book:
Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

This is the official repository of my book "Deep Learning with PyTorch Step-by-Step". Here you will find one Jupyter notebook for every chapter in the book.

Comments
  • The

    The "do-eval" option will rewrite trained model

    The second command in run.sh: main.py --do-eval --quantify --model-type roberta --prefix 0524 --filename final --task ast will rewrite the trained model checkpoint. Also it does not seem that the code has the ability to load a saved model.

    opened by zjjj 1
  • issue reproducing the results

    issue reproducing the results

    Hi there, I have ran the following command from run.sh, in an attempt to reproduce the results for roberta but I am facing discrepencie.

    python main.py --learning-rate 1e-5 --weight-decay 0 --batch-size 3 --epochs 21 --log-interval 400 \
        --grad-accum-steps 4 --model-type roberta --prefix 0524 --filename final --task ast
    

    I am getting these results, which seem different from Roberta in Table 4 where Value is 73.1% instead o 53.9.

    • Could you explain this discrepency And quick question?
    • is "intent" in Table 4 also called Action_Accuracy in the code? Thanks! image
    opened by IssamLaradji 0
  • Create python package, add type hints and structured types for dataset

    Create python package, add type hints and structured types for dataset

    Hey there, thanks for you amazing work!

    I've added a few things to your repo which I'd like to offer as a contribution:

    • Reorganized the source code into a python package that's easy to install
    • Added some type hints to a bunch of functions
    • Created a typed dict "schema" for the data so that the contents of the datasets can be easily understood directly in code
    • Simplified the argument parsing logic using dataclasses and simple-parsing

    Let me know if you have any questions. Thanks again!

    opened by lebrice 3
Owner
ASAPP Research
AI for Enterprise
ASAPP Research
Official repository for Few-shot Image Generation via Cross-domain Correspondence (CVPR '21)

Few-shot Image Generation via Cross-domain Correspondence Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zh

Utkarsh Ojha 251 Dec 11, 2022
Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

Certified Robustness to Adversarial Word Substitutions This is the official GitHub repository for the following paper: Certified Robustness to Adversa

Robin Jia 38 Oct 16, 2022
Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology Sharon Zhou, Eric Zelikman

Stanford Machine Learning Group 34 Nov 16, 2022
The repository offers the official implementation of our paper in PyTorch.

Cloth Interactive Transformer (CIT) Cloth Interactive Transformer for Virtual Try-On Bin Ren1, Hao Tang1, Fanyang Meng2, Runwei Ding3, Ling Shao4, Phi

Bingoren 49 Dec 1, 2022
Official code repository of the paper Learning Associative Inference Using Fast Weight Memory by Schlag et al.

Learning Associative Inference Using Fast Weight Memory This repository contains the offical code for the paper Learning Associative Inference Using F

Imanol Schlag 18 Oct 12, 2022
Official repository for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'21, Oral Presentation)

Official PyTorch Implementation for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'2021, Oral Presentation) HOTR: End-to-

Kakao Brain 114 Nov 28, 2022
Official repository for "Intriguing Properties of Vision Transformers" (2021)

Intriguing Properties of Vision Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, & Ming-Hsuan Yang P

Muzammal Naseer 155 Dec 27, 2022
Competitive Programming Club, Clinify's Official repository for CP problems hosting by club members.

Clinify-CPC_Programs This repository holds the record of the competitive programming club where the competitive coding aspirants are thriving hard and

Clinify Open Sauce 4 Aug 22, 2022
Official repository for "On Improving Adversarial Transferability of Vision Transformers" (2021)

Improving-Adversarial-Transferability-of-Vision-Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Khan, Fatih Porikli arxiv link A

Muzammal Naseer 47 Dec 2, 2022
This is the official repository of XVFI (eXtreme Video Frame Interpolation)

XVFI This is the official repository of XVFI (eXtreme Video Frame Interpolation), https://arxiv.org/abs/2103.16206 Last Update: 20210607 We provide th

Jihyong Oh 195 Dec 29, 2022