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| """The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark.""" |
|
|
|
|
| import textwrap |
|
|
| import pandas as pd |
|
|
| import datasets |
|
|
|
|
| _SILICONE_CITATION = """\ |
| @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.", |
| } |
| """ |
|
|
| _SILICONE_DESCRIPTION = """\ |
| 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 cover 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. |
| """ |
|
|
| _URL = "https://raw.githubusercontent.com/eusip/SILICONE-benchmark/main" |
|
|
| SWDA_DA_DESCRIPTION = { |
| "sd": "Statement-non-opinion", |
| "b": "Acknowledge (Backchannel)", |
| "sv": "Statement-opinion", |
| "%": "Uninterpretable", |
| "aa": "Agree/Accept", |
| "ba": "Appreciation", |
| "fc": "Conventional-closing", |
| "qw": "Wh-Question", |
| "nn": "No Answers", |
| "bk": "Response Acknowledgement", |
| "h": "Hedge", |
| "qy^d": "Declarative Yes-No-Question", |
| "bh": "Backchannel in Question Form", |
| "^q": "Quotation", |
| "bf": "Summarize/Reformulate", |
| 'fo_o_fw_"_by_bc': "Other", |
| 'fo_o_fw_by_bc_"': "Other", |
| "na": "Affirmative Non-yes Answers", |
| "ad": "Action-directive", |
| "^2": "Collaborative Completion", |
| "b^m": "Repeat-phrase", |
| "qo": "Open-Question", |
| "qh": "Rhetorical-Question", |
| "^h": "Hold Before Answer/Agreement", |
| "ar": "Reject", |
| "ng": "Negative Non-no Answers", |
| "br": "Signal-non-understanding", |
| "no": "Other Answers", |
| "fp": "Conventional-opening", |
| "qrr": "Or-Clause", |
| "arp_nd": "Dispreferred Answers", |
| "t3": "3rd-party-talk", |
| "oo_co_cc": "Offers, Options Commits", |
| "aap_am": "Maybe/Accept-part", |
| "t1": "Downplayer", |
| "bd": "Self-talk", |
| "^g": "Tag-Question", |
| "qw^d": "Declarative Wh-Question", |
| "fa": "Apology", |
| "ft": "Thanking", |
| "+": "Unknown", |
| "x": "Unknown", |
| "ny": "Unknown", |
| "sv_fx": "Unknown", |
| "qy_qr": "Unknown", |
| "ba_fe": "Unknown", |
| } |
|
|
| MRDA_DA_DESCRIPTION = { |
| "s": "Statement/Subjective Statement", |
| "d": "Declarative Question", |
| "b": "Backchannel", |
| "f": '"Follow-me"', |
| "q": "Question", |
| } |
|
|
| IEMOCAP_E_DESCRIPTION = { |
| "ang": "Anger", |
| "dis": "Disgust", |
| "exc": "Excitement", |
| "fea": "Fear", |
| "fru": "Frustration", |
| "hap": "Happiness", |
| "neu": "Neutral", |
| "oth": "Other", |
| "sad": "Sadness", |
| "sur": "Surprise", |
| "xxx": "Unknown", |
| } |
|
|
|
|
| class SiliconeConfig(datasets.BuilderConfig): |
| """BuilderConfig for SILICONE.""" |
|
|
| def __init__( |
| self, |
| text_features, |
| label_column, |
| data_url, |
| citation, |
| url, |
| label_classes=None, |
| **kwargs, |
| ): |
| """BuilderConfig for SILICONE. |
| Args: |
| text_features: `dict[string, string]`, map from the name of the feature |
| dict for each text field to the name of the column in the tsv file |
| label_column: `string`, name of the column in the csv/txt file corresponding |
| to the label |
| data_url: `string`, url to download the csv/text file from |
| citation: `string`, citation for the data set |
| url: `string`, url for information about the data set |
| label_classes: `list[string]`, the list of classes if the label is |
| categorical. If not provided, then the label will be of type |
| `datasets.Value('float32')`. |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(SiliconeConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
| self.text_features = text_features |
| self.label_column = label_column |
| self.label_classes = label_classes |
| self.data_url = data_url |
| self.citation = citation |
| self.url = url |
|
|
|
|
| class Silicone(datasets.GeneratorBasedBuilder): |
| """The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark.""" |
|
|
| BUILDER_CONFIGS = [ |
| SiliconeConfig( |
| name="dyda_da", |
| description=textwrap.dedent( |
| """\ |
| The DailyDialog Act Corpus contains multi-turn dialogues and is supposed to reflect daily |
| communication by covering topics about daily life. The dataset is manually labelled with |
| dialog act and emotions. It is the third biggest corpus of SILICONE with 102k utterances.""" |
| ), |
| text_features={ |
| "Utterance": "Utterance", |
| "Dialogue_Act": "Dialogue_Act", |
| "Dialogue_ID": "Dialogue_ID", |
| }, |
| label_classes=["commissive", "directive", "inform", "question"], |
| label_column="Dialogue_Act", |
| data_url={ |
| "train": _URL + "/dyda/train.csv", |
| "dev": _URL + "/dyda/dev.csv", |
| "test": _URL + "/dyda/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @InProceedings{li2017dailydialog, |
| author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi}, |
| title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset}, |
| booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)}, |
| year = {2017} |
| }""" |
| ), |
| url="http://yanran.li/dailydialog.html", |
| ), |
| SiliconeConfig( |
| name="dyda_e", |
| description=textwrap.dedent( |
| """\ |
| The DailyDialog Act Corpus contains multi-turn dialogues and is supposed to reflect daily |
| communication by covering topics about daily life. The dataset is manually labelled with |
| dialog act and emotions. It is the third biggest corpus of SILICONE with 102k utterances.""" |
| ), |
| text_features={ |
| "Utterance": "Utterance", |
| "Emotion": "Emotion", |
| "Dialogue_ID": "Dialogue_ID", |
| }, |
| label_classes=["anger", "disgust", "fear", "happiness", "no emotion", "sadness", "surprise"], |
| label_column="Emotion", |
| data_url={ |
| "train": _URL + "/dyda/train.csv", |
| "dev": _URL + "/dyda/dev.csv", |
| "test": _URL + "/dyda/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @InProceedings{li2017dailydialog, |
| author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi}, |
| title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset}, |
| booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)}, |
| year = {2017} |
| }""" |
| ), |
| url="http://yanran.li/dailydialog.html", |
| ), |
| SiliconeConfig( |
| name="iemocap", |
| description=textwrap.dedent( |
| """\ |
| The IEMOCAP database is a multi-modal database of ten speakers. It consists of dyadic |
| sessions where actors perform improvisations or scripted scenarios. Emotion categories |
| are: anger, happiness, sadness, neutral, excitement, frustration, fear, surprise, and other. |
| There is no official split of this dataset.""" |
| ), |
| text_features={ |
| "Dialogue_ID": "Dialogue_ID", |
| "Utterance_ID": "Utterance_ID", |
| "Utterance": "Utterance", |
| "Emotion": "Emotion", |
| }, |
| label_classes=list(IEMOCAP_E_DESCRIPTION.keys()), |
| label_column="Emotion", |
| data_url={ |
| "train": _URL + "/iemocap/train.csv", |
| "dev": _URL + "/iemocap/dev.csv", |
| "test": _URL + "/iemocap/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @article{busso2008iemocap, |
| title={IEMOCAP: Interactive emotional dyadic motion capture database}, |
| author={Busso, Carlos and Bulut, Murtaza and Lee, Chi-Chun and Kazemzadeh, Abe and Mower, |
| Emily and Kim, Samuel and Chang, Jeannette N and Lee, Sungbok and Narayanan, Shrikanth S}, |
| journal={Language resources and evaluation}, |
| volume={42}, |
| number={4}, |
| pages={335}, |
| year={2008}, |
| publisher={Springer} |
| }""" |
| ), |
| url="https://sail.usc.edu/iemocap/", |
| ), |
| SiliconeConfig( |
| name="maptask", |
| description=textwrap.dedent( |
| """\ |
| The HCRC MapTask Corpus was constructed through the verbal collaboration of participants |
| in order to construct a map route. This corpus is small (27k utterances). As there is |
| no standard train/dev/test split performance depends on the split.""" |
| ), |
| text_features={ |
| "Speaker": "Speaker", |
| "Utterance": "Utterance", |
| "Dialogue_Act": "Dialogue_Act", |
| }, |
| label_classes=[ |
| "acknowledge", |
| "align", |
| "check", |
| "clarify", |
| "explain", |
| "instruct", |
| "query_w", |
| "query_yn", |
| "ready", |
| "reply_n", |
| "reply_w", |
| "reply_y", |
| ], |
| label_column="Dialogue_Act", |
| data_url={ |
| "train": _URL + "/maptask/train.txt", |
| "dev": _URL + "/maptask/dev.txt", |
| "test": _URL + "/maptask/test.txt", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @inproceedings{thompson1993hcrc, |
| title={The HCRC map task corpus: natural dialogue for speech recognition}, |
| author={Thompson, Henry S and Anderson, Anne H and Bard, Ellen Gurman and Doherty-Sneddon, |
| Gwyneth and Newlands, Alison and Sotillo, Cathy}, |
| booktitle={HUMAN LANGUAGE TECHNOLOGY: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993}, |
| year={1993} |
| }""" |
| ), |
| url="http://groups.inf.ed.ac.uk/maptask/", |
| ), |
| SiliconeConfig( |
| name="meld_e", |
| description=textwrap.dedent( |
| """\ |
| The Multimodal EmotionLines Dataset enhances and extends the EmotionLines dataset where |
| multiple speakers participate in the dialogue.""" |
| ), |
| text_features={ |
| "Utterance": "Utterance", |
| "Speaker": "Speaker", |
| "Emotion": "Emotion", |
| "Dialogue_ID": "Dialogue_ID", |
| "Utterance_ID": "Utterance_ID", |
| }, |
| label_classes=["anger", "disgust", "fear", "joy", "neutral", "sadness", "surprise"], |
| label_column="Emotion", |
| data_url={ |
| "train": _URL + "/meld/train.csv", |
| "dev": _URL + "/meld/dev.csv", |
| "test": _URL + "/meld/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @article{chen2018emotionlines, |
| title={Emotionlines: An emotion corpus of multi-party conversations}, |
| author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others}, |
| journal={arXiv preprint arXiv:1802.08379}, |
| year={2018} |
| }""" |
| ), |
| url="https://affective-meld.github.io/", |
| ), |
| SiliconeConfig( |
| name="meld_s", |
| description=textwrap.dedent( |
| """\ |
| The Multimodal EmotionLines Dataset enhances and extends the EmotionLines dataset where |
| multiple speakers participate in the dialogue.""" |
| ), |
| text_features={ |
| "Utterance": "Utterance", |
| "Speaker": "Speaker", |
| "Sentiment": "Sentiment", |
| "Dialogue_ID": "Dialogue_ID", |
| "Utterance_ID": "Utterance_ID", |
| }, |
| label_classes=["negative", "neutral", "positive"], |
| label_column="Sentiment", |
| data_url={ |
| "train": _URL + "/meld/train.csv", |
| "dev": _URL + "/meld/dev.csv", |
| "test": _URL + "/meld/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @article{chen2018emotionlines, |
| title={Emotionlines: An emotion corpus of multi-party conversations}, |
| author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others}, |
| journal={arXiv preprint arXiv:1802.08379}, |
| year={2018} |
| }""" |
| ), |
| url="https://affective-meld.github.io/", |
| ), |
| SiliconeConfig( |
| name="mrda", |
| description=textwrap.dedent( |
| """\ |
| ICSI MRDA Corpus consist of transcripts of multi-party meetings hand-annotated with dialog |
| acts. It is the second biggest dataset with around 110k utterances.""" |
| ), |
| text_features={ |
| "Utterance_ID": "Utterance_ID", |
| "Dialogue_Act": "Dialogue_Act", |
| "Channel_ID": "Channel_ID", |
| "Speaker": "Speaker", |
| "Dialogue_ID": "Dialogue_ID", |
| "Utterance": "Utterance", |
| }, |
| label_classes=list(MRDA_DA_DESCRIPTION.keys()), |
| label_column="Dialogue_Act", |
| data_url={ |
| "train": _URL + "/mrda/train.csv", |
| "dev": _URL + "/mrda/dev.csv", |
| "test": _URL + "/mrda/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @techreport{shriberg2004icsi, |
| title={The ICSI meeting recorder dialog act (MRDA) corpus}, |
| author={Shriberg, Elizabeth and Dhillon, Raj and Bhagat, Sonali and Ang, Jeremy and Carvey, Hannah}, |
| year={2004}, |
| institution={INTERNATIONAL COMPUTER SCIENCE INST BERKELEY CA} |
| }""" |
| ), |
| url="https://www.aclweb.org/anthology/W04-2319", |
| ), |
| SiliconeConfig( |
| name="oasis", |
| description=textwrap.dedent( |
| """\ |
| The Bt Oasis Corpus (Oasis) contains the transcripts of live calls made to the BT and |
| operator services. This corpus is rather small (15k utterances). There is no standard |
| train/dev/test split.""" |
| ), |
| text_features={ |
| "Speaker": "Speaker", |
| "Utterance": "Utterance", |
| "Dialogue_Act": "Dialogue_Act", |
| }, |
| label_classes=[ |
| "accept", |
| "ackn", |
| "answ", |
| "answElab", |
| "appreciate", |
| "backch", |
| "bye", |
| "complete", |
| "confirm", |
| "correct", |
| "direct", |
| "directElab", |
| "echo", |
| "exclaim", |
| "expressOpinion", |
| "expressPossibility", |
| "expressRegret", |
| "expressWish", |
| "greet", |
| "hold", |
| "identifySelf", |
| "inform", |
| "informCont", |
| "informDisc", |
| "informIntent", |
| "init", |
| "negate", |
| "offer", |
| "pardon", |
| "raiseIssue", |
| "refer", |
| "refuse", |
| "reqDirect", |
| "reqInfo", |
| "reqModal", |
| "selfTalk", |
| "suggest", |
| "thank", |
| "informIntent-hold", |
| "correctSelf", |
| "expressRegret-inform", |
| "thank-identifySelf", |
| ], |
| label_column="Dialogue_Act", |
| data_url={ |
| "train": _URL + "/oasis/train.txt", |
| "dev": _URL + "/oasis/dev.txt", |
| "test": _URL + "/oasis/test.txt", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @inproceedings{leech2003generic, |
| title={Generic speech act annotation for task-oriented dialogues}, |
| author={Leech, Geoffrey and Weisser, Martin}, |
| booktitle={Proceedings of the corpus linguistics 2003 conference}, |
| volume={16}, |
| pages={441--446}, |
| year={2003}, |
| organization={Lancaster: Lancaster University} |
| }""" |
| ), |
| url="http://groups.inf.ed.ac.uk/oasis/", |
| ), |
| SiliconeConfig( |
| name="sem", |
| description=textwrap.dedent( |
| """\ |
| The SEMAINE database comes from the Sustained Emotionally coloured Human-Machine Interaction |
| using Nonverbal Expression project. This dataset has been annotated on three sentiments |
| labels: positive, negative and neutral. It is built on Multimodal Wizard of Oz experiment |
| where participants held conversations with an operator who adopted various roles designed |
| to evoke emotional reactions. There is no official split on this dataset.""" |
| ), |
| text_features={ |
| "Utterance": "Utterance", |
| "NbPairInSession": "NbPairInSession", |
| "Dialogue_ID": "Dialogue_ID", |
| "SpeechTurn": "SpeechTurn", |
| "Speaker": "Speaker", |
| "Sentiment": "Sentiment", |
| }, |
| label_classes=["Negative", "Neutral", "Positive"], |
| label_column="Sentiment", |
| data_url={ |
| "train": _URL + "/sem/train.csv", |
| "dev": _URL + "/sem/dev.csv", |
| "test": _URL + "/sem/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @article{mckeown2011semaine, |
| title={The semaine database: Annotated multimodal records of emotionally colored conversations |
| between a person and a limited agent}, |
| author={McKeown, Gary and Valstar, Michel and Cowie, Roddy and Pantic, Maja and Schroder, Marc}, |
| journal={IEEE transactions on affective computing}, |
| volume={3}, |
| number={1}, |
| pages={5--17}, |
| year={2011}, |
| publisher={IEEE} |
| }""" |
| ), |
| url="https://ieeexplore.ieee.org/document/5959155", |
| ), |
| SiliconeConfig( |
| name="swda", |
| description=textwrap.dedent( |
| """\ |
| Switchboard Dialog Act Corpus (SwDA) is a telephone speech corpus consisting of two-sided |
| telephone conversations with provided topics. This dataset includes additional features |
| such as speaker id and topic information.""" |
| ), |
| text_features={ |
| "Utterance": "Utterance", |
| "Dialogue_Act": "Dialogue_Act", |
| "From_Caller": "From_Caller", |
| "To_Caller": "To_Caller", |
| "Topic": "Topic", |
| "Dialogue_ID": "Dialogue_ID", |
| "Conv_ID": "Conv_ID", |
| }, |
| label_classes=list(SWDA_DA_DESCRIPTION.keys()), |
| label_column="Dialogue_Act", |
| data_url={ |
| "train": _URL + "/swda/train.csv", |
| "dev": _URL + "/swda/dev.csv", |
| "test": _URL + "/swda/test.csv", |
| }, |
| citation=textwrap.dedent( |
| """\ |
| @article{stolcke2000dialogue, |
| title={Dialogue act modeling for automatic tagging and recognition of conversational speech}, |
| author={Stolcke, Andreas and Ries, Klaus and Coccaro, Noah and Shriberg, Elizabeth and |
| Bates, Rebecca and Jurafsky, Daniel and Taylor, Paul and Martin, Rachel and Ess-Dykema, |
| Carol Van and Meteer, Marie}, |
| journal={Computational linguistics}, |
| volume={26}, |
| number={3}, |
| pages={339--373}, |
| year={2000}, |
| publisher={MIT Press} |
| }""" |
| ), |
| url="https://web.stanford.edu/~jurafsky/ws97/", |
| ), |
| ] |
|
|
| def _info(self): |
| features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} |
| if self.config.label_classes: |
| features["Label"] = datasets.features.ClassLabel(names=self.config.label_classes) |
| features["Idx"] = datasets.Value("int32") |
| return datasets.DatasetInfo( |
| description=_SILICONE_DESCRIPTION, |
| features=datasets.Features(features), |
| homepage=self.config.url, |
| citation=self.config.citation + "\n" + _SILICONE_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| data_files = dl_manager.download(self.config.data_url) |
| splits = [] |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "data_file": data_files["train"], |
| "split": "train", |
| }, |
| ) |
| ) |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "data_file": data_files["dev"], |
| "split": "dev", |
| }, |
| ) |
| ) |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "data_file": data_files["test"], |
| "split": "test", |
| }, |
| ) |
| ) |
| return splits |
|
|
| def _generate_examples(self, data_file, split): |
| if self.config.name not in ("maptask", "iemocap", "oasis"): |
| df = pd.read_csv(data_file, delimiter=",", header=0, quotechar='"', dtype=str)[ |
| self.config.text_features.keys() |
| ] |
|
|
| if self.config.name == "iemocap": |
| df = pd.read_csv( |
| data_file, |
| delimiter=",", |
| header=0, |
| quotechar='"', |
| names=["Dialogue_ID", "Utterance_ID", "Utterance", "Emotion", "Valence", "Activation", "Dominance"], |
| dtype=str, |
| )[self.config.text_features.keys()] |
|
|
| if self.config.name in ("maptask", "oasis"): |
| df = pd.read_csv(data_file, delimiter="|", names=["Speaker", "Utterance", "Dialogue_Act"], dtype=str)[ |
| self.config.text_features.keys() |
| ] |
|
|
| rows = df.to_dict(orient="records") |
|
|
| for n, row in enumerate(rows): |
| example = row |
| example["Idx"] = n |
|
|
| if self.config.label_column in example: |
| label = example[self.config.label_column] |
| example["Label"] = label |
|
|
| yield example["Idx"], example |
|
|
