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

Related tags

Deep Learningabcd
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"
}
Owner
ASAPP Research
AI for Enterprise
ASAPP Research
Deep Face Recognition in PyTorch

Face Recognition in PyTorch By Alexey Gruzdev and Vladislav Sovrasov Introduction A repository for different experimental Face Recognition models such

Alexey Gruzdev 141 Sep 11, 2022
Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid

SPN: Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyrami

12 Jun 27, 2022
Implementation of Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

acLSTM_motion This folder contains an implementation of acRNN for the CMU motion database written in Pytorch. See the following links for more backgro

Yi_Zhou 61 Sep 07, 2022
Rendering Point Clouds with Compute Shaders

Compute Shader Based Point Cloud Rendering This repository contains the source code to our techreport: Rendering Point Clouds with Compute Shaders and

Markus Schütz 460 Jan 05, 2023
A vision library for performing sliced inference on large images/small objects

SAHI: Slicing Aided Hyper Inference A vision library for performing sliced inference on large images/small objects Overview Object detection and insta

Open Business Software Solutions 2.3k Jan 04, 2023
Depth image based mouse cursor visual haptic

Depth image based mouse cursor visual haptic How to run it. Install pyqt5. Install python modules pip install Pillow pip install numpy For illustrati

Xiong Jie 17 Dec 20, 2022
TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Tensorflow- MaskRCNN Steps git clone https://github.com/amalaj7/TFOD-MASKRCNN.gi

Amal Ajay 2 Jan 18, 2022
Implementation of "Deep Implicit Templates for 3D Shape Representation"

Deep Implicit Templates for 3D Shape Representation Zerong Zheng, Tao Yu, Qionghai Dai, Yebin Liu. arXiv 2020. This repository is an implementation fo

Zerong Zheng 144 Dec 07, 2022
An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

알고리즘 스터디 🔥 부스트캠프 웹모바일 6기 iOS 10조의 알고리즘 스터디 입니다. 개인적인 사정 등으로 S034, S055만 참가하였습니다. 스터디 목적 상진: 코테 합격 + 부캠끝나고 아침에 일어나기 위해 필요한 사이클 기완: 꾸준하게 자리에 앉아 공부하기 +

2 Jan 11, 2022
This is the source code for the experiments related to the paper Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

Unsupervised Audio Source Separation Using Differentiable Parametric Source Models This is the source code for the experiments related to the paper Un

30 Oct 19, 2022
An All-MLP solution for Vision, from Google AI

MLP Mixer - Pytorch An All-MLP solution for Vision, from Google AI, in Pytorch. No convolutions nor attention needed! Yannic Kilcher video Install $ p

Phil Wang 784 Jan 06, 2023
Differentiable simulation for system identification and visuomotor control

gradsim gradSim: Differentiable simulation for system identification and visuomotor control gradSim is a unified differentiable rendering and multiphy

105 Dec 18, 2022
Video2x - A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR.

Official Discussion Group (Telegram): https://t.me/video2x A Discord server is also available. Please note that most developers are only on Telegram.

K4YT3X 5.9k Dec 31, 2022
This toolkit provides codes to download and pre-process the SLUE datasets, train the baseline models, and evaluate SLUE tasks.

slue-toolkit We introduce Spoken Language Understanding Evaluation (SLUE) benchmark. This toolkit provides codes to download and pre-process the SLUE

ASAPP Research 39 Sep 21, 2022
Source code for paper: Knowledge Inheritance for Pre-trained Language Models

Knowledge-Inheritance Source code paper: Knowledge Inheritance for Pre-trained Language Models (preprint). The trained model parameters (in Fairseq fo

THUNLP 31 Nov 19, 2022
Synthetic Scene Text from 3D Engines

Introduction UnrealText is a project that synthesizes scene text images using 3D graphics engine. This repository accompanies our paper: UnrealText: S

Shangbang Long 215 Dec 29, 2022
Reference code for the paper CAMS: Color-Aware Multi-Style Transfer.

CAMS: Color-Aware Multi-Style Transfer Mahmoud Afifi1, Abdullah Abuolaim*1, Mostafa Hussien*2, Marcus A. Brubaker1, Michael S. Brown1 1York University

Mahmoud Afifi 36 Dec 04, 2022
This repository contains the scripts for downloading and validating scripts for the documents

HC4: HLTCOE CLIR Common-Crawl Collection This repository contains the scripts for downloading and validating scripts for the documents. Document ids,

JHU Human Language Technology Center of Excellence 6 Jun 07, 2022
Parameterized Explainer for Graph Neural Network

PGExplainer This is a Tensorflow implementation of the paper: Parameterized Explainer for Graph Neural Network https://arxiv.org/abs/2011.04573 NeurIP

Dongsheng Luo 89 Dec 12, 2022
Vector Quantization, in Pytorch

Vector Quantization - Pytorch A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a

Phil Wang 665 Jan 08, 2023