sileod / Discovery

Mining Discourse Markers for Unsupervised Sentence Representation Learning

Discovery : Mining Discourse Markers for Unsupervised Sentence Representation Learning

Contents

The Discovery datasets consists of adjacent sentence pairs (s1,s2) with a discourse marker (y) that occured at the beginning of s2. They were extracted from the depcc web corpus.

Markers prediction can be used in order to train a sentence encoders. Discourse markers can be considered as noisy labels for various semantic tasks, such as entailment (y=therefore), subjectivity analysis (y=personally) or sentiment analysis (y=sadly), similarity (y=similarly), typicality, (y=curiously) ...

The specificity of this dataset is the diversity of the markers, since previously used data used only ~10 imbalanced classes. In this repository, you can find:

• a list of the 174 discourse markers we obtained
• a Base version of our dataset with 1.74 million pairs (10k exemples per marker)
• a Big version with 3.4 million pairs
• a Hard version with 1.74 million pairs where the connective couldn't be predicted with a fasttext linear model

Examples from the Discovery dataset:

s1	s2	y
The motivations for playing are vastly different , and yet Spin the Bottle manages to meet the needs of all its players .	It is a well crafted game .	truly,
Prefiguring The General many years later , Bernard liked nothing better than to cock a snoot at the law .	Men working on a bog , less than a mile from the Kirwan farm , dug up a human torso .	eventually,
Think a certain vertical market or knowledge about multilocations ' unique needs .	Ernest 's strength lay in the multilocation arena and gives Birch a new capability .	indeed,
@ Sklivvz : but you are implicitly using one such interpretation yourself .	One that tells you that it 's unphysical to ask anything except measurements .	namely,
Perhaps the Jeanneau 's are a bargain compared to similarly capable boats from B or C. .	Seattle , the prices for the 36 and 39 went down about 20G , a 39 now sells for a bit more than the 36 did .	locally,

Instructions

Now available on Huggingface Datasets 🤗 (GLUE-compatible format):

import datasets
datasets.load_dataset("discovery","discovery")

We also provide a script to collect new datasets in demo.ipynb

Pretrained model

We also provide a huggingface 🤗 pre-trained discourse connective prediction model built upon roberta-base. https://huggingface.co/sileod/roberta-base-discourse-marker-prediction

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sileod/roberta-base-discourse-marker-prediction")
model = AutoModelForSequenceClassification.from_pretrained("sileod/roberta-base-discourse-marker-prediction")

This model can be used as a pretrained model for discourse/pragmatics related tasks.

Discourse markers semantics:

We also provide a semantic analysis of the markers, which can be used for zero-shot classification (e.g. sentiment analysis) https://github.com/synapse-developpement/DiscSense/

Citation

@inproceedings{sileo-etal-2019-mining,
    title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
    author = "Sileo, Damien  and
      Van De Cruys, Tim  and
      Pradel, Camille  and
      Muller, Philippe",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1351",
    pages = "3477--3486",
}

The list of markers we used. PDTB markers are black, markers discovered in our work are red

Contact

For further information, you can contact:

[e-mail]