INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing

Related tags

Deep LearningINSPIRED
Overview

INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing

Existing studies on semantic parsing focus primarily on mapping a natural-language utterance to a corresponding logical form in one turn. However, because natural language can contain a great deal of ambiguity and variability, this is a difficult challenge. In this work, we investigate an interactive semantic parsing framework that explains the predicted logical form step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework, aiming to increase the transparency of the parsing process and help the user appropriately trust the final answer. To do so, we construct INSPIRED, a crowdsourced dialogue dataset derived from the ComplexWebQuestions dataset.

This repository will contain the dataset and code for our paper Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction.

Data

Dataset Download

The dataset can be downloaded under this path: ./data/dataset.jsonl

Data Structure

In the dataset file, each line is a dictionary with several keys:

{
    "id": "ID number",
    "cwq_question": "Original complex question in CWQ dataset",
    "rephrased_question": "Rephrased complex question by workers",
    "rephrased_question_label": " 'Replacement' or 'Alternative' ",
    "question": "If rephrased_question_label is marked as 'Replacement', set the value the same as rephrased_question; Otherwise, set it the same as cwq_question",
    "final_answer": "Final answer for the complex question",
    "gold_parse": "Gold sparql query for complex question",
    "preprocessed_gold_parse": "Preprocessed gold parse with entities and prefix replaced",
    "predicted_parse": "Predicted sparql query by initial semantic parser",
    "gold_sub_lfs": "A list of gold sub-logical forms after decomposition",
    "pred_sub_lfs": "A list of predicted sub-logical forms after decomposition",
    "gold_sub_qs": [
        {
          "sub_id": "ID of sub questions",
          "sub_question": "Rephrased sub question",
          "temp_sub_question": "Templated sub question for gold sub-logical form",
          "answer": "Answer for each sub question",
        }, "..."], 
    "pred_sub_qs": [
        {
          "sub_id": "ID of sub questions",
          "sub_question": "Rephrased sub question",
          "temp_sub_question": "Templated sub question for predicted sub-logical form",
          "answer": "Answer for each sub question",
        }, "..."], 
    "feedback": "A list of human feedback"
    
}
Owner
GitHub Repository
A setup script to generate ITK Python Wheels

ITK Python Package This project provides a setup.py script to build ITK Python binary packages and infrastructure to build ITK external module Python

Insight Software Consortium 59 Dec 14, 2022
The repository for the paper "When Do You Need Billions of Words of Pretraining Data?"

pretraining-learning-curves This is the repository for the paper When Do You Need Billions of Words of Pretraining Data? Edge Probing We use jiant1 fo

ML² AT CILVR 19 Nov 25, 2022
Pmapper is a super-resolution and deconvolution toolkit for python 3.6+

pmapper pmapper is a super-resolution and deconvolution toolkit for python 3.6+. PMAP stands for Poisson Maximum A-Posteriori, a highly flexible and a

NASA Jet Propulsion Laboratory 8 Nov 06, 2022
Bayesian Inference Tools in Python

BayesPy Bayesian Inference Tools in Python Our goal is, given the discrete outcomes of events, estimate the distribution of categories. Using gradient

Max Sklar 99 Dec 14, 2022
Code for the ICME 2021 paper "Exploring Driving-Aware Salient Object Detection via Knowledge Transfer"

TSOD Code for the ICME 2021 paper "Exploring Driving-Aware Salient Object Detection via Knowledge Transfer" Usage For training, open train_test, run p

Jinming Su 2 Dec 23, 2021
Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.

Thomas Roddick 219 Dec 20, 2022
This repo generates the training data and the model for Morpheus-Deblend

Morpheus-Deblend This repo generates the training data and the model for Morpheus-Deblend. This is the active development repo for the project and as

Ryan Hausen 2 Apr 18, 2022
A Python script that creates subtitles of a given length from text paragraphs that can be easily imported into any Video Editing software such as FinalCut Pro for further adjustments.

Text to Subtitles - Python This python file creates subtitles of a given length from text paragraphs that can be easily imported into any Video Editin

Dmytro North 9 Dec 24, 2022
[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution

TTSR Official PyTorch implementation of the paper Learning Texture Transformer Network for Image Super-Resolution accepted in CVPR 2020. Contents Intr

Multimedia Research 689 Dec 28, 2022
Walk with fastai

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Walk with fastai What is this p

Walk with fastai 124 Dec 10, 2022
POCO: Point Convolution for Surface Reconstruction

POCO: Point Convolution for Surface Reconstruction by: Alexandre Boulch and Renaud Marlet Abstract Implicit neural networks have been successfully use

valeo.ai 93 Dec 29, 2022
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
Embracing Single Stride 3D Object Detector with Sparse Transformer

SST: Single-stride Sparse Transformer This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer

TuSimple 385 Dec 28, 2022
ColBERT: Contextualized Late Interaction over BERT (SIGIR'20)

Update: if you're looking for ColBERTv2 code, you can find it alongside a new simpler API, in the branch new_api. ColBERT ColBERT is a fast and accura

Stanford Future Data Systems 637 Jan 08, 2023
Code corresponding to The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents

The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents This is the code corresponding to The Introspective

0 Jan 10, 2022
Accelerating BERT Inference for Sequence Labeling via Early-Exit

Sequence-Labeling-Early-Exit Code for ACL 2021 paper: Accelerating BERT Inference for Sequence Labeling via Early-Exit Requirement: Please refer to re

李孝男 23 Oct 14, 2022
OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

CMU Locus Lab 428 Dec 24, 2022
Pytorch implementation of face attention network

Face Attention Network Pytorch implementation of face attention network as described in Face Attention Network: An Effective Face Detector for the Occ

Hooks 312 Dec 09, 2022
Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung

Vending_Machine_(Mesin_Penjual_Minuman) Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung Raw Sketch untuk Essay Ringkasan P

QueenLy 1 Nov 08, 2021
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

阿才 73 Dec 16, 2022