class MeetingRecoder(AbstractDataset):
class Utterance:
def __init__(self, utterance):
self.id = utterance.utterance_id
##mapping between DAMSL and tagset used in SWDA
self.da_tag = DAMSL_TAGSET[
utterance.da_tag.strip()] - 1 # index for DAMSL starts from 1
self.speaker = utterance.speaker
self.tokens = utterance.original_text
self.length = len(self.tokens)
class Dialogue:
def __init__(self, transcript):
self.conversation_no = transcript.conversation_id
self.conversation_length = len(transcript.utterances)
self.utterances = []
for utterance in transcript.utterances:
## only consider data subset that can be tagged with mrda damsl tags
if utterance.da_tag in MRDA_DAMSL_MAP and MRDA_DAMSL_MAP[
utterance.da_tag] != "##" and MRDA_DAMSL_MAP[
utterance.da_tag] in DAMSL_TAGSET:
self.utterances.append(MeetingRecoder.Utterance(utterance))
#
def __init__(self, args, dataset_path):
self.name = type(self).__name__
corpus = mrda.CorpusReader(dataset_path)
# train, test splits standard
self.total_length = 0
self.vocabulary = Vocabulary()
self.label_set_size = len(DAMSL_TAGSET)
dataset = []
for transcript in corpus.iter_transcripts(display_progress=True):
self.total_length += 1
if args.truncate_dataset and self.total_length > 20:
break
dataset.append(MeetingRecoder.Dialogue(transcript))
#TODO: Exact test-dev split for mrda //actually do cross validation
if args.truncate_dataset:
self.train_dataset = dataset[:10]
self.valid_dataset = dataset[10:15]
self.test_dataset = dataset[15:20]
else:
self.train_dataset = dataset[:45]
self.valid_dataset = dataset[45:60]
self.test_dataset = dataset[60:]
## create vocabulary from training data (UNKS during test time)
for data_point in self.train_dataset:
for utterance in data_point.utterances:
self.vocabulary.add_and_get_indices(utterance.tokens)
if args.limit_vocabulary:
self.vocabulary.truncate()
## create character vocabulary
self.vocabulary.get_character_vocab()
class CallHomeEnglish(AbstractDataset):
class Utterance:
def __init__(self, id, utterance):
self.name = "call_home_eng"
self.id = utterance.utterance_id
self.speaker = utterance.speaker
self.tokens = utterance.tokens
self.length = len(self.tokens)
self.start_time = utterance.start_time
self.end_time = utterance.end_time
class Dialogue:
def __init__(self, transcript):
self.id = transcript.conversation_no
self.utterances = []
for id, utterance in enumerate(transcript.utterances):
self.utterances.append(CallHomeEnglish.Utterance(
id, utterance))
self.length = len(self.utterances)
def __init__(self, args, dataset_path):
self.name = type(self).__name__
corpus = call.CorpusReader(dataset_path)
self.total_length = 0
self.vocabulary = Vocabulary()
self.label_set_size = 0
dataset = []
for transcript in corpus.iter_transcripts(display_progress=True):
if args.truncate_dataset and self.total_length > 25:
break
dataset.append(CallHomeEnglish.Dialogue(transcript))
self.total_length += 1
if args.truncate_dataset:
self.train_dataset = dataset[:15]
self.valid_dataset = dataset[15:20]
self.test_dataset = dataset[20:]
else:
## depending on what task (in args) you can choose to return only a subset that is annotated for DA
self.train_dataset = dataset[:140]
self.valid_dataset = dataset[141:155]
self.test_dataset = dataset[156:]
for data_point in self.train_dataset:
for utterance in data_point.utterances:
self.vocabulary.add_and_get_indices(utterance.tokens)
if args.limit_vocabulary:
self.vocabulary.truncate()
## create character vocabulary
self.vocabulary.get_character_vocab()
class AmericanMeetingCorpus(AbstractDataset): class Utterance: def __init__(self, id, utterance): self.name = "ami" self.id = utterance.utterance_id self.label = utterance.dialogue_act self.speaker = utterance.speaker self.tokens = utterance.tokens self.length = len(self.tokens) self.start_time = utterance.start_time self.end_time = utterance.end_time class Dialogue: def __init__(self, transcript): self.id = transcript.conversation_no self.utterances = [] for id, utterance in enumerate(transcript.utterances): self.utterances.append(AmericanMeetingCorpus.Utterance(id, utterance)) self.length = len(self.utterances) def __init__(self, args, dataset_path): self.name = type(self).__name__ corpus = ami.CorpusReader(dataset_path) self.total_length = 0 self.vocabulary = Vocabulary() self.label_set_size = len(AMI_DIALOGUE_TAGSET) dataset = [] for transcript in corpus.iter_transcripts(display_progress=True): self.total_length += 1 if args.truncate_dataset and self.total_length > 25: break dataset.append(AmericanMeetingCorpus.Dialogue(transcript)) if args.truncate_dataset: self.train_dataset = dataset[:15] self.valid_dataset = dataset[15:20] self.test_dataset = dataset[20:] else: ## depending on what task (in args) you can choose to return only a subset that is annotated for DA self.train_dataset = [] self.valid_dataset = [] self.test_dataset = [] for dialogue in dataset: if dialogue.id[:-1] in TRAIN_SPLIT: self.train_dataset.append(dialogue) elif dialogue.id[:-1] in DEV_SPLIT: self.valid_dataset.append(dialogue) elif dialogue.id[:-1] in TEST_SPLIT: self.test_dataset.append(dialogue) for data_point in self.train_dataset: for utterance in data_point.utterances: self.vocabulary.add_and_get_indices(utterance.tokens) if args.limit_vocabulary: self.vocabulary.truncate() ## create character vocabulary self.vocabulary.get_character_vocab()
class SwitchBoard(AbstractDataset):
class Utterance:
## minimum elements all datasets must have; id, length, tokens
def __init__(self, id, utterance):
self.name = "swda"
self.index = utterance.utterance_index
self.id = id
self.label = DAMSL_TAGSET[utterance.damsl_act_tag().strip(
)] # index for DAMSL starts from 1
self.speaker = utterance.caller
#TODO: clean text before processing
self.tokens = utterance.text_words()
self.length = len(self.tokens)
self.pos = utterance.regularize_pos_lemmas()
class Dialogue:
## minimum elements all datasets must have; id, length, utterances
def __init__(self, transcript):
self.id = transcript.conversation_no
## length of transcript not same as number of utterances
self.length = transcript.length
self.conversation_topic = transcript.topic_description
self.utterances = []
for id, utterance in enumerate(transcript.utterances):
self.utterances.append(SwitchBoard.Utterance(id, utterance))
def __init__(self, args, dataset_path):
corpus = swda.CorpusReader(dataset_path)
self.name = type(self).__name__
self.total_length = 0
self.vocabulary = Vocabulary()
self.label_set_size = len(DAMSL_TAGSET)
dataset = []
for transcript in corpus.iter_transcripts(display_progress=True):
self.total_length += 1
if args.truncate_dataset and self.total_length > 25:
break
dataset.append(SwitchBoard.Dialogue(transcript))
shuffled_dataset = random.shuffle(dataset)
## 1155 transcribed datapoints ; 1115, 19, 21 split
if args.truncate_dataset:
self.train_dataset = dataset[:15]
self.valid_dataset = dataset[15:20]
self.test_dataset = dataset[20:]
else:
##TODO: this split adheres to numbers reporteed by Schriberg et. al., but ideally cross-validation should be done
self.train_dataset = dataset[:1115]
self.valid_dataset = dataset[1115:1134]
self.test_dataset = dataset[1134:]
## create vocabulary from training data (unks during test time)
for data_point in self.train_dataset:
for utterance in data_point.utterances:
self.vocabulary.add_and_get_indices(utterance.tokens)
if args.limit_vocabulary:
self.vocabulary.truncate()
## create character vocabulary
self.vocabulary.get_character_vocab()