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GLGE: A New General Language Generation Evaluation Benchmark

Tasks | Dataset | Leaderboard | Baselines | Paper

Updates

• 2020-10-14: Initialization.

Introduction

This repository contains information about the general langugae generation evaluation benchmark GLGE, which is composed of 8 language generation tasks, including Abstractive Text Summarization (CNN/DailyMail, Gigaword, XSUM, MSNews), Answer-aware Question Generation (SQuAD 1.1, MSQG), Conversational Question Answering (CoQA), and Personalizing Dialogue (Personachat). In order to provide more diversified difficulty challenges, we provide 3 different difficulty versions (easy, medium, and hard) for each task.

The 8 NLG tasks in GLGE:

Tasks

The 8 tasks in GLGE can be categorized into 4 groups: Abstractive Text Summarization tasks, Answer-aware Question Generation tasks, Conversational Question Answering task, and Personalizing Dialogue task.

Abstractive Text Summarization Tasks

CNN/DailyMail

CNN/DailyMail \cite{hermann2015cnndm} dataset contains 220K articles from the Daily Mail newspapers, and 93K articles from the CNN. Each article contains a bullet point summary. GLGE use the non-anonymized variant used in \cite{see2017get}. After the pre-processing, there are 311,971 <article, summary> data pairs, where the source input is the article, and the target output is the summary which consists of multiple sentences. ROUGE-1, ROUGE-2, and ROUGE-L are used as the metrics.

Gigaword

Gigaword \cite{rush2015neural} contains 4M examples extracted from the news articles of the Gigaword corpus \cite{graff2003gigaword}. After the pre-processing, there are 3,995,559 <article, summary> data pairs, where the source input is the the first sentence of the article, and the target output is the headline that often only contains a single sentence. ROUGE-1, ROUGE-2, and ROUGE-L are used as the metrics.

XSUM

XSUM \cite{narayan2018don} consists of 227K online articles from the British Broadcasting Corporation (BBC), which contains professionally written single-sentence summaries. After the pre-processing, there are 226,677 <article, summary> data pairs, where the source input is the the news article, and the target output is a single summary sentence. ROUGE-1, ROUGE-2, and ROUGE-L are used as the metrics.

MSNews

Answer-aware Question Generation Tasks

SQuAD 1.1

SQuAD 1.1 cite{rajpurkar2016squad} dataset contains 536 Wikipedia articles with over 100k Amazon Mechanical Turks crowd-worker created questions posed about the articles with the corresponding answer span. Since the original hidden test set of the SQuAD 1.1 is hidden, we re-split the dataset with the examples from the original training set and dev set. After the pre-processing, there are 98,169 <answer, passage, question> data triples the source input is a Wikipedia passage along with an answer span, and the target output is a question. ROUGE-L, BLEU-4, and METEOR are used as the metrics.

MSQG

Answer-aware Question Generation Tasks

CoQA

CoQA \cite{reddy2019coqa} dataset contains 127K questions with answers, obtained from 8k conversations about text passages from seven diverse domains. After the pre-processing, there are 116,630 <conversation history, passage, question, answer> data 4-tuples, where the source input is a sequence of conversation history along with a given question and a give passage, and the target output is a free-form answer text. F1-Score is used as the metrics.

Conversational Question Answering Task

PersonaChat

PersonaChat \cite{zhang2018personalizing} dataset is consist of 162,064 utterances, which require models generate responses according to given multi-turn conversations and persona profile. After the pre-processing, there are 151,157 <persona profile description text, conversation history, response> data triples, where the source input is a sequence of conversation history along with several sentences of persona profile description text, and the target output is a response. BLEU-1, BLEU-2, Distinct-1, and Distinct-2 are used as the metrics.

Get Dataset

In order to use our dataset, please navigate to GLGE Leaderboard and agree to our terms of service. After you do so a download link will be made available.

Run Baselines

Requirments

Recommend you install to python packages by command: pip install -r requirement.txt

Data Preparation

To preprocess all the datasets, please use the following command to tokenize the data and generate the binary data files:

cd script
./preprocessed-all.sh

Note that we tokenize each dataset with BERT-uncased tokenizer.

To preprocess a specific dataset, please use the following command:

./preprocessed.sh `<DATASET>` `<VERSION>`

For example, if you want to preprocess the easy version of CNN/DailyMail, please use the following command:

./preprocessed.sh cnndm easy

Here <DATASET> can be cnndm, gigaword, xsum, msnews, squadqg, msqg, coqa, personachat. <VERSION> can be easy, medium, hard.

Training and Testing Pipeline

Our paper provides 4 baselines, including LSTM, Transformer, ProphetNet-base, and ProphetNet-large basd on fairseq.

To train and test the baselines, please use the following command:

cd script
./run.sh `<DATASET>` `<VERSION>` `<MODEL>` `<SET>`

For example, if you want to train and test the ProphetNet-large on the medium version of SQuAD 1.1 question generation dev set, please use the following command:

./run.sh squadqg medium prophetnet dev

Here <MODEL> can be lstm, transformer, prophetnet_base, prophetnet, and <SET> can be dev, test.

If you want to use ProphetNet, please download the pretrained checkpoints at here.

Leaderboard Submission

Submissions

To submit your predictions for evaluation, please create a single folder which contains the 8 sub-folders named after each task (see reference file for an example). Inside each folder, create the prediction file using the following format: {version}.prediction where {version} is the difficulty versions (easy, medium, and hard). Please validate that you have done this correctly by evaluating against the development file. Once that is done <a href='glge@microsoft.com'>email your submission</a>. We will reply with your model performance.

Paper

If you use our benchmark or dataset, please cite our paper \cite{Liu2020GLGE}.

GLGE: A New General Language Generation Evaluation Benchmark

Additionally, since GLGE is also built out of exiting 6 datasets, please ensure you cite all of them.

An example: We evaluate our model using the GLGE benchmark \cite{Liu2020GLGE}, a general langugae generation evaluation benchmark consiting of CNN/DailyMail \cite{hermann2015cnndm} \cite{see2017get}, Gigaword \cite{rush2015neural} \cite{graff2003gigaword}, XSum \cite{narayan2018don}, MSNews, SQuAD 1.1 cite{rajpurkar2016squad}, MSQG, CoQA \cite{reddy2019coqa}, and PersonaChat \cite{zhang2018personalizing}.

Bibtex for external datasets used in GLGE:

@inproceedings{hermann2015cnndm,
  title={Teaching machines to read and comprehend},
  author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
  booktitle={NIPS},
  pages={1693--1701},
  year={2015}
}

@inproceedings{see2017get,
  title={Get to the point: Summarization with pointer-generator networks},
  author={See, Abigail and Liu, Peter J and Manning, Christopher D},
  booktitle={ACL},
  pages={1073--1083},
  year={2017}
}

@inproceedings{rush2015neural,
  title={A neural attention model for abstractive sentence summarization},
  author={Rush, Alexander M and Chopra, Sumit and Weston, Jason},
  booktitle={EMNLP},
  pages={379-389},
  year={2015}
}

@article{graff2003gigaword,
  title={English gigaword},
  author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
  journal={Linguistic Data Consortium, Philadelphia},
  volume={4},
  number={1},
  pages={34},
  year={2003}
}

@inproceedings{narayan2018don,
  title={Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization},
  author={Narayan, Shashi and Cohen, Shay B and Lapata, Mirella},
  booktitle={EMNLP},
  pages={1797--1807},
  year={2018}
}

@inproceedings{rajpurkar2016squad,
  title={Squad: 100,000+ questions for machine comprehension of text},
  author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},
  booktitle={EMNLP},
  pages={2383--2392},
  year={2016}
}

@article{reddy2019coqa,
  title={Coqa: A conversational question answering challenge},
  author={Reddy, Siva and Chen, Danqi and Manning, Christopher D},
  journal={TACL},
  volume={7},
  pages={249--266},
  year={2019}
}

@inproceedings{zhang2018personalizing,
  title={Personalizing dialogue agents: I have a dog, do you have pets too?},
  author={Zhang, Saizheng and Dinan, Emily and Urbanek, Jack and Szlam, Arthur and Kiela, Douwe and Weston, Jason},
  booktitle={ACL},
  pages={2204--2213},
  year={2018}
}

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