Download src/preprocess/DSTC2.py from OpenDFM/DialogZoo: direct link, hf CLI and curl.
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4.88 kB
| from utils import parse, read_json_file, write_jsonl_file, write_json_file | |
| import os | |
| def parse_dialogue_act(dialogue_acts, utterance, domain): | |
| parsed_dialogue_acts = [] | |
| for da in dialogue_acts: | |
| svt = [] | |
| for slot, value in da["slots"]: | |
| value = str(value) | |
| # request | |
| if slot == "slot": | |
| svt.append({"slot": value}) | |
| else: | |
| if value in utterance: | |
| start = utterance.index(value) | |
| end = start + len(value) | |
| else: | |
| start = -1 | |
| end = -1 | |
| svt.append( | |
| { | |
| "slot": slot, | |
| "values": [ | |
| { | |
| "value": value, | |
| "start": start, | |
| "end": end, | |
| } | |
| ], | |
| "relation": "=", | |
| } | |
| ) | |
| dialogue_act = { | |
| "act": da["act"], | |
| "slot_value_table": svt, | |
| "domain": domain, | |
| } | |
| parsed_dialogue_acts.append(dialogue_act) | |
| return parsed_dialogue_acts | |
| def preprocess( | |
| input_dir, | |
| output_dir, | |
| split, | |
| domain="restaurant", | |
| intent="FindRestaurants", | |
| write=True, | |
| ): | |
| processed_data = [] | |
| schema = { | |
| domain: set(), | |
| } | |
| with open(os.path.join(input_dir, f"scripts/config/{split}.flist"), "r") as reader: | |
| for example_dir_name in reader: | |
| example_dir = os.path.join(input_dir, "data", example_dir_name.strip()) | |
| data = read_json_file(os.path.join(example_dir, "log.json")) | |
| label = read_json_file(os.path.join(example_dir, "label.json")) | |
| dialog = {"turn": "multi", "locale": "en", "dialog": []} | |
| for turn_idx, turn_data in enumerate(data["turns"]): | |
| # system | |
| utterance = turn_data["output"]["transcript"] | |
| dialog["dialog"].append( | |
| { | |
| "roles": ["SYSTEM"], | |
| "utterance": utterance, | |
| "dialogue_acts": parse_dialogue_act( | |
| turn_data["output"]["dialog-acts"], utterance, domain | |
| ), | |
| } | |
| ) | |
| # user | |
| label_turn = label["turns"][turn_idx] | |
| # 1-best | |
| # utterance = label_turn["transcription"] | |
| utterance = turn_data["input"]["live"]["asr-hyps"][0]["asr-hyp"] | |
| dialog["dialog"].append( | |
| { | |
| "roles": ["USER"], | |
| "utterance": utterance, | |
| "belief_state": [ | |
| { | |
| "intent": intent, | |
| "informed_slot_value_table": [], | |
| "requested_slots": label_turn["requested-slots"], | |
| "domain": domain, | |
| } | |
| ], | |
| "dialogue_acts": parse_dialogue_act( | |
| label_turn["semantics"]["json"], utterance, domain | |
| ), | |
| } | |
| ) | |
| for slot, value in label_turn["goal-labels"].items(): | |
| dialog["dialog"][-1]["belief_state"][-1][ | |
| "informed_slot_value_table" | |
| ].append( | |
| { | |
| "slot": slot, | |
| "values": [ | |
| { | |
| "value": value, | |
| } | |
| ], | |
| "relation": "=", | |
| } | |
| ) | |
| schema[domain].add(slot) | |
| processed_data.append(dialog) | |
| ontology = {domain: {slot: True for slot in schema[domain]}} | |
| if write: | |
| outfile = os.path.join(output_dir, f"{split}.jsonl") | |
| write_jsonl_file(processed_data, outfile) | |
| if "train" in split: | |
| split = "train" | |
| elif "dev" in split: | |
| split = "dev" | |
| elif "test" in split: | |
| split = "test" | |
| write_json_file( | |
| ontology, os.path.join(args.output_dir, f"{split}_ontology.json") | |
| ) | |
| return processed_data, schema | |
| if __name__ == "__main__": | |
| args = parse() | |
| preprocess(os.path.join(args.input_dir, "traindev"), args.output_dir, "dstc2_train") | |
| preprocess(os.path.join(args.input_dir, "traindev"), args.output_dir, "dstc2_dev") | |
| preprocess(os.path.join(args.input_dir, "test"), args.output_dir, "dstc2_test") | |