annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: conversational_weather
tags:
- data-to-text
Dataset Card for GEM/conversational_weather
Dataset Description
- Homepage: [Needs More Information]
- Repository: https://github.com/facebookresearch/TreeNLG
- Paper: https://aclanthology.org/P19-1080
- Leaderboard: N/A
- Point of Contact: Kartikeya Upasani
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., _ARG_TEMP_ 34).
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/conversational_weather')
The data loader can be found here.
paper
authors
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
Dataset Overview
Where to find the Data and its Documentation
Download
Paper
BibTex
@inproceedings{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1080",
doi = "10.18653/v1/P19-1080",
pages = "831--844"
}
Contact Name
Kartikeya Upasani
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
no
Covered Languages
English
License
cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
Intended Use
This dataset is intended to help develop conversational agents that exhibit human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
Primary Task
Data-to-Text
Communicative Goal
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
Credit
Curation Organization Type(s)
industry
Curation Organization(s)
Dataset Creators
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
Funding
Who added the Dataset to GEM?
Vipul Raheja (Grammarly)
Dataset Structure
Data Fields
gem_id: (string): GEM-formatted row idid: (string): Row id in the original datauser_query: (string): Natural language weather query from humanstree_str_mr: (string): Synthetically-added user context (datetime and location) in the form of a tree-structured MRresponse: (string): A tree-structured annotation of the response.
Example Instance
{'gem_id': 'weather-train-11',
'id': '1108963',
'synthetic_user_context': '[__DG_INFORM__ [__ARG_TASK__ get_forecast ] '
'[__ARG_TEMP__ 37 ] [__ARG_TEMP_UNIT__ fahrenheit ] '
'[__ARG_CLOUD_COVERAGE__ partly cloudy ] '
'[__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ currently ] '
'] [__ARG_LOCATION__ [__ARG_CITY__ Oakland ] '
'[__ARG_COUNTRY__ United States ] [__ARG_REGION__ '
'California ] ] ] [__DG_INFORM__ [__ARG_TASK__ '
'get_forecast ] [__ARG_TEMP_SUMMARY__ mid 40s ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ] [__DG_INFORM__ '
'[__ARG_TASK__ get_forecast ] '
'[__ARG_CLOUD_COVERAGE__ mostly sunny ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ]',
'tree_str_mr': "[__DG_INFORM__ It's [__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ "
'currently ] ] [__ARG_CLOUD_COVERAGE__ partly cloudy ] and '
'[__ARG_TEMP__ __ARG_TEMP__ ] [__ARG_TEMP_UNIT__ '
'__ARG_TEMP_UNIT__ ] [__ARG_LOCATION__ in [__ARG_CITY__ '
'__ARG_CITY__ ] , [__ARG_REGION__ __ARG_REGION__ ] , '
'[__ARG_COUNTRY__ __ARG_COUNTRY__ ] ] . ] [__DG_INFORM__ '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This afternoon ] '
"] , it'll be [__ARG_CLOUD_COVERAGE__ mostly sunny ] ] "
'[__DG_INFORM__ with temperatures in the [__ARG_TEMP_SUMMARY__ '
'mid <number> ] ]',
'user_query': 'Show weather forecast for Oakland, CA. '}
Data Splits
- Standard Splits: Train/Validation/Test
- Additional Split: Disc_Test (a more challenging subset of the test set that contains discourse relations)
Splitting Criteria
The test set contains 3,121 examples, of which 1.1K (35%) have unique MRs that have never been seen in the training set.
{'gem_id': 'weather-train-13333', 'data_id': '1260610', 'user_query': 'Sundown', 'tree_str_mr': '[__DG_INFORM__ [__ARG_TASK__ get_weather_attribute ] [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ 05:04 PM ] ] ]', 'response': '[__DG_INFORM__ The sun will go down at [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ __ARG_TIME__ ] ] ]'}
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
The dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
The dataset offers rich tree-based meaning representations that offer fine-grained control over the response, e.g. by specifying which two attributes are to be contrasted. The natural language input queries are also provided to model the coherence of the response based on the input. The output response is annotated with the input meaning components using special bracketing tokens, which enables developing new techniques such as constrained decoding to improve quality of output responses
Similar Datasets
no
Ability that the Dataset measures
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
GEM-Specific Curation
Modificatied for GEM?
yes
GEM Modifications
data points removed
Modification Details
The original repo contained a challenge set disc_test.tsv, which is a subset of the test set consisting of discourse relations (CONTRAST and JUSTIFY) , but also contained JOIN relations.
This discrepancy has been rectified in the GEM version. The rectified version has been added in the challenge_sets
Additional Splits?
no
Getting Started with the Task
Previous Results
Previous Results
Measured Model Abilities
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
Metrics
BLEU, Other: Other Metrics
Other Metrics
Tree accuracy: It measures whether the tree structure in the prediction matches that of the input MR exactly (modulo repeated arguments that need only appear once).
Proposed Evaluation
Automatic metrics are evaluated on the raw model predictions (which have de-lexicalized fields):
- Tree accuracy: Measures whether the tree structure in the prediction matches that of the input MR exactly.
- BLEU-4: A word overlap metric commonly used for evaluating NLG systems.
Authors also performed human evaluation studies by asking annotators to evaluate the quality of responses produced by different models. Annotators provided binary ratings on the following dimensions: • Grammaticality: Measures fluency of the responses. • Correctness: Measures semantic correctness of the responses.
Previous results available?
no
Dataset Curation
Original Curation
Original Curation Rationale
The dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes. To achieve this, the dataset contains rich tree-structured meaning representations that are specified using several data arguments and discourse acts, the input natural language queries, and annotations for the responses.
Communicative Goal
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
Sourced from Different Sources
no
Language Data
How was Language Data Obtained?
Crowdsourced, Machine-generated
Where was it crowdsourced?
Other crowdworker platform
Topics Covered
The dataset is focused on the weather domain: Weather was the first successful case of NLG put into production back in the 80s (Reiter & Dale, 1997). This domain offers significant complexity for NLG. Weather forecast summaries in particular can be very long, and require reasoning over several disjoint pieces of information.
Data Validation
validated by crowdworker
Data Preprocessing
Please refer to Appendix D of the original paper for details.
Was Data Filtered?
hybrid
Filter Criteria
Please refer to Appendix C of the original paper for details.
Structured Annotations
Additional Annotations?
none
Annotation Service?
no
Consent
Any Consent Policy?
no
Justification for Using the Data
Annotation was done as work for hire and contains no PII.
Private Identifying Information (PII)
Contains PII?
no PII
Justification for no PII
Data is simulated and not specific to annotator.
Maintenance
Any Maintenance Plan?
no
Broader Social Context
Previous Work on the Social Impact of the Dataset
Usage of Models based on the Data
no
Impact on Under-Served Communities
Addresses needs of underserved Communities?
no
Discussion of Biases
Any Documented Social Biases?
unsure
Are the Language Producers Representative of the Language?
Grammatical evaluations performed with the data to date have used norms from informal Standard American English. These prescriptive notions of grammaticality potentially serve to perpetuate systemic power imbalances as they’re conveyed by language.
Since the data only contains informal Standard American English, its use to train a model may not be appropriate depending on the potential use case.
Considerations for Using the Data
PII Risks and Liability
Potential PII Risk
Annotation was done as work for hire and contains no PII. Annotated data is simulated and not specific to annotator.
Licenses
Known Technical Limitations
Unsuited Applications
An imperfect model used to convey actual weather data could mislead users about weather conditions?