Utterance
stringlengths
1
1.41k
Dialogue_Act
stringclasses
4 values
Dialogue_ID
stringlengths
1
5
Label
class label
4 classes
Idx
int32
0
87.2k
say , jim , how about going for a few beers after dinner ?
directive
1
1directive
0
you know that is tempting but is really not good for our fitness .
commissive
1
0commissive
1
what do you mean ? it will help us to relax .
question
1
3question
2
do you really think so ? i don't . it will just make us fat and act silly . remember last time ?
question
1
3question
3
i guess you are right.but what shall we do ? i don't feel like sitting at home .
question
1
3question
4
i suggest a walk over to the gym where we can play singsong and meet some of our friends .
directive
1
1directive
5
that's a good idea . i hear mary and sally often go there to play pingpong.perhaps we can make a foursome with them .
commissive
1
0commissive
6
sounds great to me ! if they are willing , we could ask them to go dancing with us.that is excellent exercise and fun , too .
inform
1
2inform
7
good.let ' s go now .
directive
1
1directive
8
all right .
commissive
1
0commissive
9
can you do push-ups ?
question
2
3question
10
of course i can . it's a piece of cake ! believe it or not , i can do 30 push-ups a minute .
inform
2
2inform
11
really ? i think that's impossible !
question
2
3question
12
you mean 30 push-ups ?
question
2
3question
13
yeah !
inform
2
2inform
14
it's easy . if you do exercise everyday , you can make it , too .
inform
2
2inform
15
can you study with the radio on ?
question
3
3question
16
no , i listen to background music .
inform
3
2inform
17
what is the difference ?
question
3
3question
18
the radio has too many comerials .
inform
3
2inform
19
that's true , but then you have to buy a record player .
inform
3
2inform
20
are you all right ?
question
4
3question
21
i will be all right soon . i was terrified when i watched them fall from the wire .
inform
4
2inform
22
don't worry.he is an acrobat 。
inform
4
2inform
23
i see .
inform
4
2inform
24
hey john , nice skates . are they new ?
question
5
3question
25
yeah , i just got them . i started playing ice hockey in a community league . so , i finally got myself new skates .
inform
5
2inform
26
what position do you play ?
question
5
3question
27
i ’ m a defender . it ’ s a lot of fun . you don ’ t have to be able to skate as fast on defense .
inform
5
2inform
28
yeah , you ’ re a pretty big guy . i play goalie , myself .
inform
5
2inform
29
oh , yeah ? which team ?
question
5
3question
30
the rockets .
inform
5
2inform
31
really ? i think we play you guys next week . well , i have to go to practice . see you later .
directive
5
1directive
32
all right , see you later .
commissive
5
0commissive
33
hey lydia , what are you reading ?
question
6
3question
34
i ’ m looking at my horoscope for this month ! my outlook is very positive . it says that i should take a vacation to someplace exotic , and that i will have a passionate summer fling !
inform
6
2inform
35
what are you talking about ? let me see that ... what are horoscopes ?
question
6
3question
36
it ’ s a prediction of your month , based on your zodiac sign . you have a different sign for the month and date you were born in . i was born on april 15th , so i ’ m an aries . when were you born ?
question
6
3question
37
january 5th .
inform
6
2inform
38
let ’ s see . . . you ’ re a capricorn . it says that you will be feeling stress at work , but you could see new , exciting developments in your love life . looks like we ’ ll both have interesting summers !
inform
6
2inform
39
that ’ s bogus . i don't feel any stress at work , and my love life is practically nonexistent . this zodiac stuff is all a bunch of nonsense .
inform
6
2inform
40
no , it ’ s not , your astrology sign can tell you a lot about your personality . see ? it says that an aries is energetic and loves to socialize .
inform
6
2inform
41
well , you certainly match those criteria , but they ’ re so broad they could apply to anyone . what does it say about me ?
question
6
3question
42
a capricorn is serious-minded and practical . she likes to do things in conventional ways . that sounds just like you !
inform
6
2inform
43
frank ’ s getting married , do you believe this ?
question
7
3question
44
is he really ?
question
7
3question
45
yes , he is . he loves the girl very much .
inform
7
2inform
46
who is he marring ?
question
7
3question
47
a girl he met on holiday in spain , i think .
inform
7
2inform
48
have they set a date for the wedding ?
question
7
3question
49
not yet .
inform
7
2inform
50
i hear you bought a new house in the northern suburbs .
inform
8
2inform
51
that ’ s right , we bought it the same day we came on the market .
inform
8
2inform
52
what kind of house is it ?
question
8
3question
53
it ’ s a wonderful spanish style .
inform
8
2inform
54
oh , i love the roof tiles on spanish style houses .
inform
8
2inform
55
and it ’ s a bargaining . a house like this in river side costs double the price .
inform
8
2inform
56
great , is it a two bedroom house ?
question
8
3question
57
no , it has three bedrooms and three beds , and has a living room with a twelve-foot ceiling . there ’ s a two-car garage .
inform
8
2inform
58
that ’ s a nice area too . it ’ ll be a good investment for you .
inform
8
2inform
59
yeas , when will you buy a house ?
question
8
3question
60
not untill the end of this year , you know , just before my wedding .
inform
8
2inform
61
right , congratulations .
inform
8
2inform
62
thank you .
inform
8
2inform
63
hi , becky , what's up ?
question
9
3question
64
not much , except that my mother-in-law is driving me up the wall .
inform
9
2inform
65
what's the problem ?
question
9
3question
66
she loves to nit-pick and criticizes everything that i do . i can never do anything right when she's around .
inform
9
2inform
67
for example ?
question
9
3question
68
well , last week i invited her over to dinner . my husband and i had no problem with the food , but if you listened to her , then it would seem like i fed her old meat and rotten vegetables . there's just nothing can please her .
inform
9
2inform
69
no , i can't see that happening . i know you're a good cook and nothing like that would ever happen .
inform
9
2inform
70
it's not just that . she also criticizes how we raise the kids .
inform
9
2inform
71
my mother-in-law used to do the same thing to us . if it wasn't disciplining them enough , then we were disciplining them too much . she also complained about the food we fed them , the schools we sent them too , and everything else under the sun .
inform
9
2inform
72
you said she used to ? how did you stop her ?
question
9
3question
73
we basically sat her down and told her how we felt about her constant criticizing , and how we welcomed her advice but hoped she'd let us do our things . she understood , and now everything is a lot more peaceful .
inform
9
2inform
74
that sounds like a good idea . i'll have to try that .
inform
9
2inform
75
how are zina's new programmers working out ?
question
10
3question
76
i hate to admit it , but they're good . and fast . the filipino kid is a genius .
inform
10
2inform
77
so you'll make the stars.com deadline , and have us up and running next week ?
question
10
3question
78
it'll be close , but we'll make it .
inform
10
2inform
79
good . after stars.com starts paying us , we won't need vikam's cash anymore .
inform
10
2inform
80
and if we don't need them , we won't need zina , either .
inform
10
2inform
81
do you like cooking ?
question
11
3question
82
yes . i like cooking very much . i got this hobby when i was 12 years sold .
inform
11
2inform
83
why do you like it ?
question
11
3question
84
i have no idea . i like cooking by myself . i like to taste delicious food .
inform
11
2inform
85
that's wonderful !
inform
11
2inform
86
and i love trying new recipes , which i usually test with my friends . you can come , too .
directive
11
1directive
87
really ? i hope i can have a chance to taste it . don't forget to tell me .
commissive
11
0commissive
88
certainly .
inform
11
2inform
89
anyone home ? jen !
question
12
3question
90
i'm in the kitchen ... let yourself in !
inform
12
2inform
91
wow ! you're really working up a storm !
inform
12
2inform
92
i know . i've even worked up a sweat .
inform
12
2inform
93
you look like a cooking show host--only messier .
inform
12
2inform
94
you look so tan and healthy !
inform
13
2inform
95
thanks . i just got back from summer camp .
inform
13
2inform
96
how was it ?
question
13
3question
97
great . i got to try so many things for the first time .
inform
13
2inform
98
like what ?
question
13
3question
99

Dataset Card for SILICONE Benchmark

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.",
}

Contributions

Thanks to @eusip and @lhoestq for adding this dataset.

Downloads last month
1,139
Edit dataset card

Models trained or fine-tuned on silicone