Dataset Card for NLUCat

Name: NLUCat
Creator: Projecte Aina
License: https://choosealicense.com/licenses/cc-by-4.0/

Dataset Summary

NLUCat is a dataset of NLU in Catalan. It consists of nearly 12,000 instructions annotated with the most relevant intents and spans. Each instruction is accompanied, in addition, by the instructions received by the annotator who wrote it.

The intents taken into account are the habitual ones of a virtual home assistant (activity calendar, IOT, list management, leisure, etc.), but specific ones have also been added to take into account social and healthcare needs for vulnerable people (information on administrative procedures, menu and medication reminders, etc.).

The spans have been annotated with a tag describing the type of information they contain. They are fine-grained, but can be easily grouped to use them in robust systems.

The examples are not only written in Catalan, but they also take into account the geographical and cultural reality of the speakers of this language (geographic points, cultural references, etc.)

This dataset can be used to train models for intent classification, spans identification and examples generation.

This is a simplified version of the dataset for training and evaluating intent classifiers. The full dataset and the annotation guideslines can be found in Zenodo

This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY 4.0. Give appropriate credit , provide a link to the license, and indicate if changes were made.

Supported Tasks and Leaderboards

Intent classification, spans identification and examples generation.

Languages

The dataset is in Catalan (ca-ES).

Dataset Structure

Data Instances

Three JSON files, one for each split.

Data Fields

example: str. Example
annotation: dict. Annotation of the example
intent: str. Intent tag
slots: list. List of slots
Tag:str. tag to the slot
Text:str. Text of the slot
Start_char: int. First character of the span
End_char: int. Last character of the span

Example

An example looks as follows:

       {
            "example": "Demana una ambulància; la meva dona està de part.",
            "annotation": {
                "intent": "call_emergency",
                "slots": [
                    {
                        "Tag": "service",
                        "Text": "ambulància",
                        "Start_char": 11,
                        "End_char": 21
                    },
                    {
                        "Tag": "situation",
                        "Text": "la meva dona està de part",
                        "Start_char": 23,
                        "End_char": 48
                    }
                ]
            }
        },

Data Splits

NLUCat.train: 9128 examples
NLUCat.dev: 1441 examples
NLUCat.test: 1441 examples

Statistics

	test	dev	train	Total
alarm_query	14	9	68	91
alarm_remove	10	12	68	90
alarm_set	11	10	69	90
app_end	8	9	43	60
app_launch	9	7	47	63
audio_volume_down	15	16	105	136
audio_volume_mute	8	9	62	79
audio_volume_up	14	16	101	131
book restaurant	31	27	182	240
calendar_query	34	38	227	299
calendar_remove	31	33	211	275
calendar_set	50	53	340	443
call_emergency	14	18	111	143
call_medicalService	14	11	70	95
call_person	23	18	116	157
call_service	6	9	45	60
compare_places	6	7	47	60
contact_add	20	22	138	180
contact_query	16	16	89	121
cooking_query	13	12	65	90
cooking_recipe	9	10	74	93
datetime_convert	14	14	95	123
datetime_query	18	17	112	147
general_affirm	6	6	18	30
general_commandstop	13	13	75	101
general_confirm	6	6	48	60
general_dontcare	8	6	46	60
general_explain	5	5	7	17
general_greet	13	10	67	90
general_joke	10	11	69	90
general_negate	12	9	69	90
general_praise	15	10	65	90
general_quirky	15	14	99	128
general_repeat	11	14	65	90
generat_explain	8	7	58	73
iot_cleaning	11	9	70	90
iot_coffee	10	12	68	90
iot_hue_lightchange	9	12	69	90
iot_hue_lightdim	14	12	64	90
iot_hue_lightoff	10	11	70	91
iot_hue_lighton	11	14	66	91
iot_hue_lightup	10	9	70	89
iot_wemo_off	11	13	65	89
iot_wemo_on	6	8	46	60
lists_createoradd	19	16	115	150
lists_query	15	15	92	122
lists_remove	14	14	91	119
medReminder_query	18	17	108	143
medReminder_set	17	17	113	147
medicalAppointment_query	20	19	114	153
medicalAppointment_set	24	23	165	212
menu_query	15	17	113	145
message_query	21	20	140	181
message_send	26	24	162	212
music_dislikeness	10	9	69	88
music_likeness	11	9	71	91
music_query	22	23	135	180
music_settings	9	9	63	81
news_query	19	22	149	190
play_audiobook	12	15	93	120
play_game	12	11	67	90
play_music	41	45	271	357
play_podcasts	20	19	121	160
play_radio	20	20	115	155
play_video	15	15	90	120
qa_currency	12	9	69	90
qa_definition	19	23	147	189
qa_factoid	26	24	143	193
qa_maths	13	12	95	120
qa_medicalService	20	21	117	158
qa_procedures	36	33	220	289
qa_service	16	18	112	146
qa_sports	9	9	72	90
qa_stock	13	10	67	90
recommendation_events	22	22	143	187
recommendation_locations	23	24	157	204
recommendation_movies	18	23	139	180
share_currentLocation	15	13	92	120
social_post	19	20	112	151
social_query	14	14	96	124
takeaway_order	20	25	135	180
takeaway_query	7	9	50	66
transport_directions	28	24	181	233
transport_query	31	31	185	247
transport_taxi	26	22	132	180
transport_ticket	25	25	160	210
transport_traffic	15	17	88	120
weather_query	31	29	189	249
Total	1440	1440	9117	11997

Dataset Creation

Curation Rationale

We created this dataset to contribute to the development of language models in Catalan, a low-resource language.

When creating this dataset, we took into account not only the language but the entire socio-cultural reality of the Catalan-speaking population. Special consideration was also given to the needs of the vulnerable population.

Source Data

Initial Data Collection and Normalization

We commissioned a company to create fictitious examples for the creation of this dataset.

Who are the source language producers?

We commissioned the writing of the examples to the company m47 labs.

Annotations

Annotation process

The elaboration of this dataset has been done in three steps, taking as a model the process followed by the NLU-Evaluation-Data dataset, as explained in the paper.

First step: translation or elaboration of the instructions given to the annotators to write the examples.
Second step: writing the examples. This step also includes the grammatical correction and normalization of the texts.
Third step: recording the attempts and the slots of each example. In this step, some modifications were made to the annotation guides to adjust them to the real situations.

Who are the annotators?

The drafting of the examples and their annotation was entrusted to the company m47 labs through a public tender process.

Personal and Sensitive Information

No personal or sensitive information included.

The examples used for the preparation of this dataset are fictitious and, therefore, the information shown is not real.

Considerations for Using the Data

Social Impact of Dataset

We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account, and that it will especially help to improve the quality of life of people with special needs.

Discussion of Biases

When writing the examples, the annotators were asked to take into account the socio-cultural reality (geographic points, artists and cultural references, etc.) of the Catalan-speaking population. Likewise, they were asked to be careful to avoid examples that reinforce the stereotypes that exist in this society. For example: be careful with the gender or origin of personal names that are associated with certain activities.

Other Known Limitations

[N/A]

Additional Information

Dataset Curators

Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es)

This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

Licensing Information