annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialogue-modeling
- language-modeling
- masked-language-modeling
- sentiment-classification
- text-scoring
pretty_name: SILICONE Benchmark
tags:
- emotion-classification
- dialogue-act-classification
dataset_info:
- config_name: dyda_da
features:
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': commissive
'1': directive
'2': inform
'3': question
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 8346638
num_examples: 87170
- name: validation
num_bytes: 764277
num_examples: 8069
- name: test
num_bytes: 740226
num_examples: 7740
download_size: 8874925
dataset_size: 9851141
- config_name: dyda_e
features:
- name: Utterance
dtype: string
- name: Emotion
dtype: string
- name: Dialogue_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': anger
'1': disgust
'2': fear
'3': happiness
'4': no emotion
'5': sadness
'6': surprise
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 8547111
num_examples: 87170
- name: validation
num_bytes: 781445
num_examples: 8069
- name: test
num_bytes: 757670
num_examples: 7740
download_size: 8874925
dataset_size: 10086226
- config_name: iemocap
features:
- name: Dialogue_ID
dtype: string
- name: Utterance_ID
dtype: string
- name: Utterance
dtype: string
- name: Emotion
dtype: string
- name: Label
dtype:
class_label:
names:
'0': ang
'1': dis
'2': exc
'3': fea
'4': fru
'5': hap
'6': neu
'7': oth
'8': sad
'9': sur
'10': xxx
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 908180
num_examples: 7213
- name: validation
num_bytes: 100969
num_examples: 805
- name: test
num_bytes: 254248
num_examples: 2021
download_size: 1158778
dataset_size: 1263397
- config_name: maptask
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Label
dtype:
class_label:
names:
'0': acknowledge
'1': align
'2': check
'3': clarify
'4': explain
'5': instruct
'6': query_w
'7': query_yn
'8': ready
'9': reply_n
'10': reply_w
'11': reply_y
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 1260413
num_examples: 20905
- name: validation
num_bytes: 178184
num_examples: 2963
- name: test
num_bytes: 171806
num_examples: 2894
download_size: 1048357
dataset_size: 1610403
- config_name: meld_e
features:
- name: Utterance
dtype: string
- name: Speaker
dtype: string
- name: Emotion
dtype: string
- name: Dialogue_ID
dtype: string
- name: Utterance_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': anger
'1': disgust
'2': fear
'3': joy
'4': neutral
'5': sadness
'6': surprise
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 916337
num_examples: 9989
- name: validation
num_bytes: 100234
num_examples: 1109
- name: test
num_bytes: 242352
num_examples: 2610
download_size: 1553014
dataset_size: 1258923
- config_name: meld_s
features:
- name: Utterance
dtype: string
- name: Speaker
dtype: string
- name: Sentiment
dtype: string
- name: Dialogue_ID
dtype: string
- name: Utterance_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 930405
num_examples: 9989
- name: validation
num_bytes: 101801
num_examples: 1109
- name: test
num_bytes: 245873
num_examples: 2610
download_size: 1553014
dataset_size: 1278079
- config_name: mrda
features:
- name: Utterance_ID
dtype: string
- name: Dialogue_Act
dtype: string
- name: Channel_ID
dtype: string
- name: Speaker
dtype: string
- name: Dialogue_ID
dtype: string
- name: Utterance
dtype: string
- name: Label
dtype:
class_label:
names:
'0': s
'1': d
'2': b
'3': f
'4': q
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 9998857
num_examples: 83943
- name: validation
num_bytes: 1143286
num_examples: 9815
- name: test
num_bytes: 1807462
num_examples: 15470
download_size: 10305848
dataset_size: 12949605
- config_name: oasis
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Label
dtype:
class_label:
names:
'0': accept
'1': ackn
'2': answ
'3': answElab
'4': appreciate
'5': backch
'6': bye
'7': complete
'8': confirm
'9': correct
'10': direct
'11': directElab
'12': echo
'13': exclaim
'14': expressOpinion
'15': expressPossibility
'16': expressRegret
'17': expressWish
'18': greet
'19': hold
'20': identifySelf
'21': inform
'22': informCont
'23': informDisc
'24': informIntent
'25': init
'26': negate
'27': offer
'28': pardon
'29': raiseIssue
'30': refer
'31': refuse
'32': reqDirect
'33': reqInfo
'34': reqModal
'35': selfTalk
'36': suggest
'37': thank
'38': informIntent-hold
'39': correctSelf
'40': expressRegret-inform
'41': thank-identifySelf
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 887018
num_examples: 12076
- name: validation
num_bytes: 112185
num_examples: 1513
- name: test
num_bytes: 119254
num_examples: 1478
download_size: 802002
dataset_size: 1118457
- config_name: sem
features:
- name: Utterance
dtype: string
- name: NbPairInSession
dtype: string
- name: Dialogue_ID
dtype: string
- name: SpeechTurn
dtype: string
- name: Speaker
dtype: string
- name: Sentiment
dtype: string
- name: Label
dtype:
class_label:
names:
'0': Negative
'1': Neutral
'2': Positive
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 496168
num_examples: 4264
- name: validation
num_bytes: 57896
num_examples: 485
- name: test
num_bytes: 100072
num_examples: 878
download_size: 513689
dataset_size: 654136
- config_name: swda
features:
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: From_Caller
dtype: string
- name: To_Caller
dtype: string
- name: Topic
dtype: string
- name: Dialogue_ID
dtype: string
- name: Conv_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': sd
'1': b
'2': sv
'3': '%'
'4': aa
'5': ba
'6': fc
'7': qw
'8': nn
'9': bk
'10': h
'11': qy^d
'12': bh
'13': ^q
'14': bf
'15': fo_o_fw_"_by_bc
'16': fo_o_fw_by_bc_"
'17': na
'18': ad
'19': ^2
'20': b^m
'21': qo
'22': qh
'23': ^h
'24': ar
'25': ng
'26': br
'27': 'no'
'28': fp
'29': qrr
'30': arp_nd
'31': t3
'32': oo_co_cc
'33': aap_am
'34': t1
'35': bd
'36': ^g
'37': qw^d
'38': fa
'39': ft
'40': +
'41': x
'42': ny
'43': sv_fx
'44': qy_qr
'45': ba_fe
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 20499788
num_examples: 190709
- name: validation
num_bytes: 2265898
num_examples: 21203
- name: test
num_bytes: 291471
num_examples: 2714
download_size: 16227500
dataset_size: 23057157
config_names:
- dyda_da
- dyda_e
- iemocap
- maptask
- meld_e
- meld_s
- mrda
- oasis
- sem
- swda
Dataset Card for SILICONE Benchmark
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: [N/A]
- Repository: https://github.com/eusip/SILICONE-benchmark
- Paper: https://arxiv.org/abs/2009.11152
- Leaderboard: [N/A]
- Point of Contact: Ebenge Usip
Dataset Summary
The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a variety of domains including daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant labels.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English.
Dataset Structure
Data Instances
DailyDialog Act Corpus (Dialogue Act)
For the dyda_da configuration one example from the dataset is:
{
'Utterance': "the taxi drivers are on strike again .",
'Dialogue_Act': 2, # "inform"
'Dialogue_ID': "2"
}
DailyDialog Act Corpus (Emotion)
For the dyda_e configuration one example from the dataset is:
{
'Utterance': "'oh , breaktime flies .'",
'Emotion': 5, # "sadness"
'Dialogue_ID': "997"
}
Interactive Emotional Dyadic Motion Capture (IEMOCAP) database
For the iemocap configuration one example from the dataset is:
{
'Dialogue_ID': "Ses04F_script03_2",
'Utterance_ID': "Ses04F_script03_2_F025",
'Utterance': "You're quite insufferable. I expect it's because you're drunk.",
'Emotion': 0, # "ang"
}
HCRC MapTask Corpus
For the maptask configuration one example from the dataset is:
{
'Speaker': "f",
'Utterance': "i think that would bring me over the crevasse",
'Dialogue_Act': 4, # "explain"
}
Multimodal EmotionLines Dataset (Emotion)
For the meld_e configuration one example from the dataset is:
{
'Utterance': "'Push 'em out , push 'em out , harder , harder .'",
'Speaker': "Joey",
'Emotion': 3, # "joy"
'Dialogue_ID': "1",
'Utterance_ID': "2"
}
Multimodal EmotionLines Dataset (Sentiment)
For the meld_s configuration one example from the dataset is:
{
'Utterance': "'Okay , y'know what ? There is no more left , left !'",
'Speaker': "Rachel",
'Sentiment': 0, # "negative"
'Dialogue_ID': "2",
'Utterance_ID': "4"
}
ICSI MRDA Corpus
For the mrda configuration one example from the dataset is:
{
'Utterance_ID': "Bed006-c2_0073656_0076706",
'Dialogue_Act': 0, # "s"
'Channel_ID': "Bed006-c2",
'Speaker': "mn015",
'Dialogue_ID': "Bed006",
'Utterance': "keith is not technically one of us yet ."
}
BT OASIS Corpus
For the oasis configuration one example from the dataset is:
{
'Speaker': "b",
'Utterance': "when i rang up um when i rang to find out why she said oh well your card's been declined",
'Dialogue_Act': 21, # "inform"
}
SEMAINE database
For the sem configuration one example from the dataset is:
{
'Utterance': "can you think of somebody who is like that ?",
'NbPairInSession': "11",
'Dialogue_ID': "59",
'SpeechTurn': "674",
'Speaker': "Agent",
'Sentiment': 1, # "Neutral"
}
Switchboard Dialog Act (SwDA) Corpus
For the swda configuration one example from the dataset is:
{
'Utterance': "but i 'd probably say that 's roughly right .",
'Dialogue_Act': 33, # "aap_am"
'From_Caller': "1255",
'To_Caller': "1087",
'Topic': "CRIME",
'Dialogue_ID': "818",
'Conv_ID': "sw2836",
}
Data Fields
For the dyda_da configuration, the different fields are:
Utterance: Utterance as a string.Dialogue_Act: Dialog act label of the utterance. It can be one of "commissive" (0), "directive" (1), "inform" (2) or "question" (3).Dialogue_ID: identifier of the dialogue as a string.
For the dyda_e configuration, the different fields are:
Utterance: Utterance as a string.Dialogue_Act: Dialog act label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "happiness" (3), "no emotion" (4), "sadness" (5) or "surprise" (6).Dialogue_ID: identifier of the dialogue as a string.
For the iemocap configuration, the different fields are:
Dialogue_ID: identifier of the dialogue as a string.Utterance_ID: identifier of the utterance as a string.Utterance: Utterance as a string.Emotion: Emotion label of the utterance. It can be one of "Anger" (0), "Disgust" (1), "Excitement" (2), "Fear" (3), "Frustration" (4), "Happiness" (5), "Neutral" (6), "Other" (7), "Sadness" (8), "Surprise" (9) or "Unknown" (10).
For the maptask configuration, the different fields are:
Speaker: identifier of the speaker as a string.Utterance: Utterance as a string.Dialogue_Act: Dialog act label of the utterance. It can be one of "acknowledge" (0), "align" (1), "check" (2), "clarify" (3), "explain" (4), "instruct" (5), "query_w" (6), "query_yn" (7), "ready" (8), "reply_n" (9), "reply_w" (10) or "reply_y" (11).
For the meld_e configuration, the different fields are:
Utterance: Utterance as a string.Speaker: Speaker as a string.Emotion: Emotion label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "joy" (3), "neutral" (4), "sadness" (5) or "surprise" (6).Dialogue_ID: identifier of the dialogue as a string.Utterance_ID: identifier of the utterance as a string.
For the meld_s configuration, the different fields are:
Utterance: Utterance as a string.Speaker: Speaker as a string.Sentiment: Sentiment label of the utterance. It can be one of "negative" (0), "neutral" (1) or "positive" (2).Dialogue_ID: identifier of the dialogue as a string.Utterance_ID: identifier of the utterance as a string.
For the mrda configuration, the different fields are:
Utterance_ID: identifier of the utterance as a string.Dialogue_Act: Dialog act label of the utterance. It can be one of "s" (0) [Statement/Subjective Statement], "d" (1) [Declarative Question], "b" (2) [Backchannel], "f" (3) [Follow-me] or "q" (4) [Question].Channel_ID: identifier of the channel as a string.Speaker: identifier of the speaker as a string.Dialogue_ID: identifier of the channel as a string.Utterance: Utterance as a string.
For the oasis configuration, the different fields are:
Speaker: identifier of the speaker as a string.Utterance: Utterance as a string.Dialogue_Act: Dialog act label of the utterance. It can be one of "accept" (0), "ackn" (1), "answ" (2), "answElab" (3), "appreciate" (4), "backch" (5), "bye" (6), "complete" (7), "confirm" (8), "correct" (9), "direct" (10), "directElab" (11), "echo" (12), "exclaim" (13), "expressOpinion"(14), "expressPossibility" (15), "expressRegret" (16), "expressWish" (17), "greet" (18), "hold" (19), "identifySelf" (20), "inform" (21), "informCont" (22), "informDisc" (23), "informIntent" (24), "init" (25), "negate" (26), "offer" (27), "pardon" (28), "raiseIssue" (29), "refer" (30), "refuse" (31), "reqDirect" (32), "reqInfo" (33), "reqModal" (34), "selfTalk" (35), "suggest" (36), "thank" (37), "informIntent-hold" (38), "correctSelf" (39), "expressRegret-inform" (40) or "thank-identifySelf" (41).
For the sem configuration, the different fields are:
Utterance: Utterance as a string.NbPairInSession: number of utterance pairs in a dialogue.Dialogue_ID: identifier of the dialogue as a string.SpeechTurn: SpeakerTurn as a string.Speaker: Speaker as a string.Sentiment: Sentiment label of the utterance. It can be "Negative", "Neutral" or "Positive".
For the swda configuration, the different fields are:
Utterance: Utterance as a string.
Dialogue_Act: Dialogue act label of the utterance. It can be "sd" (0) [Statement-non-opinion], "b" (1) [Acknowledge (Backchannel)], "sv" (2) [Statement-opinion], "%" (3) [Uninterpretable], "aa" (4) [Agree/Accept], "ba" (5) [Appreciation], "fc" (6) [Conventional-closing], "qw" (7) [Wh-Question], "nn" (8) [No Answers], "bk" (9) [Response Acknowledgement], "h" (10) [Hedge], "qy^d" (11) [Declarative Yes-No-Question], "bh" (12) [Backchannel in Question Form], "^q" (13) [Quotation], "bf" (14) [Summarize/Reformulate], 'fo_o_fw_"by_bc' (15) [Other], 'fo_o_fw_by_bc"' (16) [Other], "na" (17) [Affirmative Non-yes Answers], "ad" (18) [Action-directive], "^2" (19) [Collaborative Completion], "b^m" (20) [Repeat-phrase], "qo" (21) [Open-Question], "qh" (22) [Rhetorical-Question], "^h" (23) [Hold Before Answer/Agreement], "ar" (24) [Reject], "ng" (25) [Negative Non-no Answers], "br" (26) [Signal-non-understanding], "no" (27) [Other Answers], "fp" (28) [Conventional-opening], "qrr" (29) [Or-Clause], "arp_nd" (30) [Dispreferred Answers], "t3" (31) [3rd-party-talk], "oo_co_cc" (32) [Offers, Options Commits], "aap_am" (33) [Maybe/Accept-part], "t1" (34) [Downplayer], "bd" (35) [Self-talk], "^g" (36) [Tag-Question], "qw^d" (37) [Declarative Wh-Question], "fa" (38) [Apology], "ft" (39) [Thanking], "+" (40) [Unknown], "x" (41) [Unknown], "ny" (42) [Unknown], "sv_fx" (43) [Unknown], "qy_qr" (44) [Unknown] or "ba_fe" (45) [Unknown].
From_Caller: identifier of the from caller as a string.
To_Caller: identifier of the to caller as a string.
Topic: Topic as a string.
Dialogue_ID: identifier of the dialogue as a string.
Conv_ID: identifier of the conversation as a string.
Data Splits
| Dataset name | Train | Valid | Test |
|---|---|---|---|
| dyda_da | 87170 | 8069 | 7740 |
| dyda_e | 87170 | 8069 | 7740 |
| iemocap | 7213 | 805 | 2021 |
| maptask | 20905 | 2963 | 2894 |
| meld_e | 9989 | 1109 | 2610 |
| meld_s | 9989 | 1109 | 2610 |
| mrda | 83944 | 9815 | 15470 |
| oasis | 12076 | 1513 | 1478 |
| sem | 4264 | 485 | 878 |
| swda | 190709 | 21203 | 2714 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Benchmark Curators
Emile Chapuis, Pierre Colombo, Ebenge Usip.
Licensing Information
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.
Citation Information
@inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239",
doi = "10.18653/v1/2020.findings-emnlp.239",
pages = "2636--2648",
abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.",
}