A crowdsourced dataset of dialogues grounded in social contexts involving utilization of commonsense.

Overview

Commonsense-Dialogues Dataset

We present Commonsense-Dialogues, a crowdsourced dataset of ~11K dialogues grounded in social contexts involving utilization of commonsense. The social contexts used were sourced from the train split of the SocialIQA dataset, a multiple-choice question-answering based social commonsense reasoning benchmark.

For the collection of the Commonsense-Dialogues dataset, each Turker was presented a social context and asked to write a dialogue of 4-6 turns between two people based on the event(s) described in the context. The Turker was asked to alternate between the roles of an individual referenced in the context and a 3rd party friend. See the following dialogues as examples:

    "1": {  # dialogue_id
        "context": "Sydney met Carson's mother for the first time last week. He liked her.",   # multiple individuals in the context: Sydney and Carson
        "speaker": "Sydney",   # role 1 = Sydney, role 2 = a third-person friend of Sydney
        "turns": [
            "I met Carson's mother last week for the first time.",
            "How was she?",
            "She turned out to be really nice. I like her.",
            "That's good to hear.",
            "It is, especially since Carson and I are getting serious.",
            "Well, at least you'll like your in-law if you guys get married."
        ]
    }

    "2": {
        "context": "Kendall had a party at Jordan's house but was found out to not have asked and just broke in.",
        "speaker": "Kendall",
        "turns": [
            "Did you hear about my party this weekend at Jordan\u2019s house?",
            "I heard it was amazing, but that you broke in.",
            "That was a misunderstanding, I had permission to be there.",
            "Who gave you permission?",
            "I talked to Jordan about it months ago before he left town to go to school, but he forgot to tell his roommates about it.",
            "Ok cool, I hope everything gets resolved."
        ]
    }

The data can be found in the /data directory of this repo. train.json has ~9K dialogues, valid.json and test.json have ~1K dialogues each. Since all the contexts were sourced from the train split of SocialIQA, it is imperative to note that any form of multi-task training and evaluation with Commonsense-Dialogues and SocialIQA must be done with caution to ensure fair and accurate conclusions.

Some statistics about the data are provided below:

Stat Train Valid Test
# of dialogues 9058 1157 1158
average # of turns in a dialogue 5.72 5.72 5.71
average # of words in a turn 12.4 12.4 12.2
# of distinct SocialIQA contexts used 3672 483 473
average # of dialogues for a SocialIQA context 2.46 2.395 2.45

Security

See CONTRIBUTING for more information.

License

This repository is licensed under the CC-BY-NC 4.0 License.

Citation

If you use this dataset, please cite the following paper:

@inproceedings{zhou-etal-2021-commonsense,
    title = "Commonsense-Focused Dialogues for Response Generation: An Empirical Study",
    author = "Zhou, Pei  and
      Gopalakrishnan, Karthik  and
      Hedayatnia, Behnam  and
      Kim, Seokhwan  and
      Pujara, Jay  and
      Ren, Xiang  and
      Liu, Yang  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    year = "2021",
    address = "Singapore and Online",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2109.06427"
}

Note that the paper uses newly collected dialogues as well as those that were filtered from existing datasets. This repo contains our newly collected dialogues alone.

Owner
Alexa
Alexa
Search with BERT vectors in Solr and Elasticsearch

Search with BERT vectors in Solr and Elasticsearch

Dmitry Kan 123 Dec 29, 2022
Material for GW4SHM workshop, 16/03/2022.

GW4SHM Workshop Wednesday, 16th March 2022 (13:00 – 15:15 GMT): Presented by: Dr. Rhodri Nelson, Imperial College London Project website: https://www.

Devito Codes 1 Mar 16, 2022
BeautyNet is an AI powered model which can tell you whether you're beautiful or not.

BeautyNet BeautyNet is an AI powered model which can tell you whether you're beautiful or not. Download Dataset from here:https://www.kaggle.com/gpios

Ansh Gupta 0 May 06, 2022
NLP, Machine learning

Netflix-recommendation-system NLP, Machine learning About Recommendation algorithms are at the core of the Netflix product. It provides their members

Harshith VH 6 Jan 12, 2022
Train BPE with fastBPE, and load to Huggingface Tokenizer.

BPEer Train BPE with fastBPE, and load to Huggingface Tokenizer. Description The BPETrainer of Huggingface consumes a lot of memory when I am training

Lizhuo 1 Dec 23, 2021
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production

Provides an implementation of today's most used tokenizers, with a focus on performance and versatility. Main features: Train new vocabularies and tok

Hugging Face 6.2k Dec 31, 2022
Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

Natural Language Processing @UCLA 464 Jan 04, 2023
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Jan 03, 2023
Repo for Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization

ESACL: Enhanced Seq2Seq Autoencoder via Contrastive Learning for AbstractiveText Summarization This repo is for our paper "Enhanced Seq2Seq Autoencode

Rachel Zheng 14 Nov 01, 2022
A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to ach

Keon Lee 237 Jan 02, 2023
無料で使える中品質なテキスト読み上げソフトウェア、VOICEVOXの音声合成エンジン

VOICEVOX ENGINE VOICEVOXの音声合成エンジン。 実態は HTTP サーバーなので、リクエストを送信すればテキスト音声合成できます。 API ドキュメント VOICEVOX ソフトウェアを起動した状態で、ブラウザから

Hiroshiba 3 Jul 05, 2022
Grading tools for Advanced NLP (11-711)Grading tools for Advanced NLP (11-711)

Grading tools for Advanced NLP (11-711) Installation You'll need docker and unzip to use this repo. For docker, visit the official guide to get starte

Hao Zhu 2 Sep 27, 2022
This repository details the steps in creating a Part of Speech tagger using Trigram Hidden Markov Models and the Viterbi Algorithm without using external libraries.

POS-Tagger This repository details the creation of a Part-of-Speech tagger using Trigram Hidden Markov Models to predict word tags in a word sequence.

Raihan Ahmed 1 Dec 09, 2021
MRC approach for Aspect-based Sentiment Analysis (ABSA)

B-MRC MRC approach for Aspect-based Sentiment Analysis (ABSA) Paper: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extracti

Phuc Phan 1 Apr 05, 2022
Mapping a variable-length sentence to a fixed-length vector using BERT model

Are you looking for X-as-service? Try the Cloud-Native Neural Search Framework for Any Kind of Data bert-as-service Using BERT model as a sentence enc

Han Xiao 11.1k Jan 01, 2023
[NeurIPS 2021] Code for Learning Signal-Agnostic Manifolds of Neural Fields

Learning Signal-Agnostic Manifolds of Neural Fields This is the uncleaned code for the paper Learning Signal-Agnostic Manifolds of Neural Fields. The

60 Dec 12, 2022
The Classical Language Toolkit

Notice: This Git branch (dev) contains the CLTK's upcoming major release (v. 1.0.0). See https://github.com/cltk/cltk/tree/master and https://docs.clt

Classical Language Toolkit 754 Jan 09, 2023
CMeEE 数据集医学实体抽取

医学实体抽取_GlobalPointer_torch 介绍 思想来自于苏神 GlobalPointer,原始版本是基于keras实现的,模型结构实现参考现有 pytorch 复现代码【感谢!】,基于torch百分百复现苏神原始效果。 数据集 中文医学命名实体数据集 点这里申请,很简单,共包含九类医学

85 Dec 28, 2022
This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

Word-Level Coreference Resolution This is a repository with the code to reproduce the experiments described in the paper of the same name, which was a

79 Dec 27, 2022
KoBERTopic은 BERTopic을 한국어 데이터에 적용할 수 있도록 토크나이저와 BERT를 수정한 코드입니다.

KoBERTopic 모델 소개 KoBERTopic은 BERTopic을 한국어 데이터에 적용할 수 있도록 토크나이저와 BERT를 수정했습니다. 기존 BERTopic : https://github.com/MaartenGr/BERTopic/tree/05a6790b21009d

Won Joon Yoo 26 Jan 03, 2023