dstc3

This is a text classification dataset. It is intended for machine learning research and experimentation.

This dataset is obtained via formatting another publicly available data to be compatible with our AutoIntent Library.

Usage

It is intended to be used with our AutoIntent Library:

from autointent import Dataset

dstc3 = Dataset.from_datasets("AutoIntent/dstc3")

Source

This dataset is taken from marcel-gohsen/dstc3 and formatted with our AutoIntent Library:

from datasets import load_dataset
from autointent import Dataset

# load original data
dstc3 = load_dataset("marcel-gohsen/dstc3")

# extract intent names
dstc3["test"] = dstc3["test"].filter(lambda example: example["transcript"] != "")
intent_names = sorted(set(name for intents in dstc3["test"]["intent"] for name in intents))
intent_names.remove("reqmore")
dstc3["test"].filter(lambda example: "reqmore" in example["intent"])
name_to_id = {name: i for i, name in enumerate(intent_names)}

# parse complicated dstc format
def transform(example: dict):
    return {
        "utterance": example["transcript"],
        "label": [name_to_id[intent_name] for intent_name in example["intent"] if intent_name != "reqmore"],
    }
dstc_converted = dstc3["test"].map(transform, remove_columns=dstc3["test"].features.keys())

# format to autointent.Dataset
intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
utterances = []
oos_utterances = []
for rec in dstc_converted.to_list():
    if len(rec["label"]) == 0:
        rec.pop("label")
        oos_utterances.append(rec["utterance"])
    else:
        utterances.append(rec)
oos_records = [{"utterance": ut} for ut in set(oos_utterances)]
dstc_converted = Dataset.from_dict({"intents": intents, "train": utterances + oos_records})