Datasets:

pfb30
/

multi_woz_v22

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- crowdsourced
+- machine-generated
+languages:
+- en
+licenses:
+- apache-2-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+- structure-prediction
+- text-classification
+task_ids:
+- dialogue-modeling
+- multi-class-classification
+- parsing
+---
+# Dataset Card for MultiWOZ
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+## Dataset Description
+- **Repository:** [MultiWOZ 2.2 github repository](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2)
+- **Paper:** [MultiWOZ v2](https://arxiv.org/abs/1810.00278), and [MultiWOZ v2.2](https://www.aclweb.org/anthology/2020.nlp4convai-1.13.pdf)
+- **Point of Contact:** [Paweł Budzianowski](pfb30@cam.ac.uk)
+### Dataset Summary
+Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
+MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
+improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fixes dialogue state annotation errors
+across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values
+(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots.
+### Supported Tasks and Leaderboards
+This dataset supports a range of task.
+- **Generative dialogue modeling** or `dialogue-modeling`: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-[BLEU](https://huggingface.co/metrics/bleu), inform rate and request success.
+- **Intent state tracking**, a `multi-class-classification` task: predict the belief state of the user side of the conversation, performance is measured by [F1](https://huggingface.co/metrics/f1).
+- **Dialog act prediction**, a `parsing` task: parse an utterance into the corresponding dialog acts for the system to use. [F1](https://huggingface.co/metrics/f1) is typically reported.
+### Languages
+The text in the dataset is in English (`en`).
+## Dataset Structure
+### Data Instances
+A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has a single utterance, e.g.:
+```
+['What fun places can I visit in the East?',
+ 'We have five spots which include boating, museums and entertainment. Any preferences that you have?']
+```
+The utterances of the `USER` are also annotated with frames denoting their intent and believe state:
+```
+[{'service': ['attraction'],
+  'slots': [{'copy_from': [],
+    'copy_from_value': [],
+    'exclusive_end': [],
+    'slot': [],
+    'start': [],
+    'value': []}],
+  'state': [{'active_intent': 'find_attraction',
+    'requested_slots': [],
+    'slots_values': {'slots_values_list': [['east']],
+     'slots_values_name': ['attraction-area']}}]},
+ {'service': [], 'slots': [], 'state': []}]
+```
+Finally, each of the utterances is annotated with dialog acts which provide a structured representation of what the `USER` or `SYSTEM` is inquiring or giving information about.
+```
+[{'dialog_act': {'act_slots': [{'slot_name': ['east'],
+     'slot_value': ['area']}],
+   'act_type': ['Attraction-Inform']},
+  'span_info': {'act_slot_name': ['area'],
+   'act_slot_value': ['east'],
+   'act_type': ['Attraction-Inform'],
+   'span_end': [39],
+   'span_start': [35]}},
+ {'dialog_act': {'act_slots': [{'slot_name': ['none'], 'slot_value': ['none']},
+    {'slot_name': ['boating', 'museums', 'entertainment', 'five'],
+     'slot_value': ['type', 'type', 'type', 'choice']}],
+   'act_type': ['Attraction-Select', 'Attraction-Inform']},
+  'span_info': {'act_slot_name': ['type', 'type', 'type', 'choice'],
+   'act_slot_value': ['boating', 'museums', 'entertainment', 'five'],
+   'act_type': ['Attraction-Inform',
+    'Attraction-Inform',
+    'Attraction-Inform',
+    'Attraction-Inform'],
+   'span_end': [40, 49, 67, 12],
+   'span_start': [33, 42, 54, 8]}}]
+```
+### Data Fields
+Each dialogue instance has the following fields:
+- `dialogue_id`: a unique ID identifying the dialog. The MUL and PMUL names refer to strictly multi domain dialogues (at least 2 main domains are involved) while the SNG, SSNG and WOZ names refer to single domain dialogues with potentially sub-domains like booking.
+- `services`: a list of services mentioned in the dialog, such as `train` or `hospitals`.
+- `turns`: the sequence of utterances with their annotations, including:
+  - `turn_id`: a turn identifier, unique per dialog.
+  - `speaker`: either the `USER` or `SYSTEM`.
+  - `utterance`: the text of the utterance.
+  - `dialogue_acts`: The structured parse of the utterance into dialog acts in the system's grammar
+    - `act_type`: Such as e.g. `Attraction-Inform` to seek or provide information about an `attraction`
+    - `act_slots`: provide more details about the action
+    - `span_info`: maps these `act_slots` to the `utterance` text.
+  - `frames`: only for `USER` utterances, track the user's belief state, i.e. a structured representation of what they are trying to achieve in the fialog. This decomposes into:
+    - `service`: the service they are interested in
+    - `state`: their belief state including their `active_intent` and further information expressed in `requested_slots`
+    - `slots`: a mapping of the `requested_slots` to where they are mentioned in the text. It takes one of two forms, detailed next:
+The first type are span annotations that identify the location where slot values have been mentioned in the utterances for non-categorical slots. These span annotations are represented as follows:
+```
+{
+  "slots": [
+    {
+      "slot": String of slot name.
+      "start": Int denoting the index of the starting character in the utterance corresponding to the slot value.
+      "exclusive_end": Int denoting the index of the character just after the last character corresponding to the slot value in the utterance. In python, utterance[start:exclusive_end] gives the slot value.
+      "value": String of value. It equals to utterance[start:exclusive_end], where utterance is the current utterance in string.
+    }
+  ]
+}
+```
+There are also some non-categorical slots whose values are carried over from another slot in the dialogue state. Their values don"t explicitly appear in the utterances. For example, a user utterance can be "I also need a taxi from the restaurant to the hotel.", in which the state values of "taxi-departure" and "taxi-destination" are respectively carried over from that of "restaurant-name" and "hotel-name". For these slots, instead of annotating them as spans, a "copy from" annotation identifies the slot it copies the value from. This annotation is formatted as follows,
+```
+{
+  "slots": [
+    {
+      "slot": Slot name string.
+      "copy_from": The slot to copy from.
+      "value": A list of slot values being . It corresponds to the state values of the "copy_from" slot.
+    }
+  ]
+}
+```
+### Data Splits
+The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
+|                            | Tain   | Valid | Test |
+| -----                      | ------ | ----- | ---- |
+| Number of dialogues        | 8438   | 1000  | 1000 |
+| Number of turns            | 42190  | 5000  | 5000 |
+## 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
+### Dataset Curators
+The initial dataset (Versions 1.0 and 2.0) was created by a team of researchers from the [Cambridge Dialogue Systems Group](https://mi.eng.cam.ac.uk/research/dialogue/corpora/). Version 2.1 was developed on top of v2.0 by a team from Amazon, and v2.2 was developed by a team of Google researchers.
+### Licensing Information
+The dataset is released under the Apache License 2.0.
+### Citation Information
+You can cite the following for the various versions of MultiWOZ:
+Version 1.0
+```
+@inproceedings{ramadan2018large,
+  title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing},
+  author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica},
+  booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
+  volume={2},
+  pages={432--437},
+  year={2018}
+}
+```
+Version 2.0
+```
+@inproceedings{budzianowski2018large,
+    Author = {Budzianowski, Pawe{\l} and Wen, Tsung-Hsien and Tseng, Bo-Hsiang  and Casanueva, I{\~n}igo and Ultes Stefan and Ramadan Osman and Ga{\v{s}}i\'c, Milica},
+    title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling},
+    booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+    year={2018}
+}
+```
+Version 2.1
+```
+@article{eric2019multiwoz,
+  title={MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines},
+  author={Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek},
+  journal={arXiv preprint arXiv:1907.01669},
+  year={2019}
+}
+```
+Version 2.2
+```
+@inproceedings{zang2020multiwoz,
+  title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines},
+  author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong},
+  booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020},
+  pages={109--117},
+  year={2020}
+}
+```

dataset_infos.json ADDED Viewed

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MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fizes dialogue state annotation errors\nacross 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values\n(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots.\n", "citation": "@article{corr/abs-2007-12720,\n author = {Xiaoxue Zang and\n Abhinav Rastogi and\n Srinivas Sunkara and\n Raghav Gupta and\n Jianguo Zhang and\n Jindong Chen},\n title = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections\n and State Tracking Baselines},\n journal = {CoRR},\n volume = {abs/2007.12720},\n year = {2020},\n url = {https://arxiv.org/abs/2007.12720},\n archivePrefix = {arXiv},\n eprint = {2007.12720}\n}\n", "homepage": "https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2", "license": "Apache License 2.0", "features": 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utterances in the original version, resulting in an\nimproved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fizes dialogue state annotation errors\nacross 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values\n(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots.\n", "citation": "@article{corr/abs-2007-12720,\n author = {Xiaoxue Zang and\n Abhinav Rastogi and\n Srinivas Sunkara and\n Raghav Gupta and\n Jianguo Zhang and\n Jindong Chen},\n title = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections\n and State Tracking Baselines},\n journal = {CoRR},\n volume = {abs/2007.12720},\n year = {2020},\n url = {https://arxiv.org/abs/2007.12720},\n archivePrefix = {arXiv},\n eprint = {2007.12720}\n}\n", "homepage": "https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2", "license": "Apache License 2.0", "features": {"dialogue_id": {"dtype": "string", "id": null, "_type": "Value"}, "services": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "turns": {"feature": {"turn_id": {"dtype": "string", "id": null, "_type": "Value"}, "speaker": {"num_classes": 2, "names": ["USER", "SYSTEM"], "names_file": null, "id": null, "_type": "ClassLabel"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}, "frames": {"feature": {"service": {"dtype": "string", "id": null, "_type": "Value"}, "state": {"active_intent": {"dtype": "string", "id": null, "_type": "Value"}, "requested_slots": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "slots_values": {"feature": {"slots_values_name": {"dtype": "string", "id": null, "_type": "Value"}, "slots_values_list": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "slots": {"feature": {"slot": {"dtype": "string", "id": null, "_type": "Value"}, "value": {"dtype": "string", "id": null, "_type": "Value"}, "start": {"dtype": "int32", "id": null, "_type": "Value"}, "exclusive_end": {"dtype": "int32", "id": null, "_type": "Value"}, "copy_from": {"dtype": "string", "id": null, "_type": "Value"}, "copy_from_value": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "dialogue_acts": {"dialog_act": {"feature": {"act_type": {"dtype": "string", "id": null, "_type": "Value"}, "act_slots": {"feature": {"slot_name": {"dtype": "string", "id": null, "_type": "Value"}, "slot_value": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "span_info": {"feature": {"act_type": {"dtype": 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	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:43bbff0fc91bf2c1824b1010b0385a2c26f4af287597cd9f2c36581c128492b2
+size 14772

multi_woz_v22.py ADDED Viewed

	@@ -0,0 +1,278 @@

+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MultiWOZ v2.2: Multi-domain Wizard of OZ version 2.2"""
+from __future__ import absolute_import, division, print_function
+import json
+import datasets
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{corr/abs-2007-12720,
+  author    = {Xiaoxue Zang and
+               Abhinav Rastogi and
+               Srinivas Sunkara and
+               Raghav Gupta and
+               Jianguo Zhang and
+               Jindong Chen},
+  title     = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections
+               and State Tracking Baselines},
+  journal   = {CoRR},
+  volume    = {abs/2007.12720},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2007.12720},
+  archivePrefix = {arXiv},
+  eprint    = {2007.12720}
+}
+"""
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
+MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
+improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fizes dialogue state annotation errors
+across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values
+(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots.
+"""
+_LICENSE = "Apache License 2.0"
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL_LIST = [
+    ("dialogue_acts", "https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json")
+]
+_URL_LIST += [
+    (
+        f"train_{i:03d}",
+        f"https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_{i:03d}.json",
+    )
+    for i in range(1, 18)
+]
+_URL_LIST += [
+    (
+        f"dev_{i:03d}",
+        f"https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_{i:03d}.json",
+    )
+    for i in range(1, 3)
+]
+_URL_LIST += [
+    (
+        f"test_{i:03d}",
+        f"https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_{i:03d}.json",
+    )
+    for i in range(1, 3)
+]
+_URLs = dict(_URL_LIST)
+class MultiWozV22(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("2.2.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="v2.2", version=datasets.Version("2.2.0"), description="MultiWOZ v2.2"),
+        datasets.BuilderConfig(
+            name="v2.2_active_only",
+            version=datasets.Version("2.2.0"),
+            description="MultiWOZ v2.2, only keeps around frames with an active intent",
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = "v2.2_active_only"
+    def _info(self):
+        features = datasets.Features(
+            {
+                "dialogue_id": datasets.Value("string"),
+                "services": datasets.Sequence(datasets.Value("string")),
+                "turns": datasets.Sequence(
+                    {
+                        "turn_id": datasets.Value("string"),
+                        "speaker": datasets.ClassLabel(names=["USER", "SYSTEM"]),
+                        "utterance": datasets.Value("string"),
+                        "frames": datasets.Sequence(
+                            {
+                                "service": datasets.Value("string"),
+                                "state": {
+                                    "active_intent": datasets.Value("string"),
+                                    "requested_slots": datasets.Sequence(datasets.Value("string")),
+                                    "slots_values": datasets.Sequence(
+                                        {
+                                            "slots_values_name": datasets.Value("string"),
+                                            "slots_values_list": datasets.Sequence(datasets.Value("string")),
+                                        }
+                                    ),
+                                },
+                                "slots": datasets.Sequence(
+                                    {
+                                        "slot": datasets.Value("string"),
+                                        "value": datasets.Value("string"),
+                                        "start": datasets.Value("int32"),
+                                        "exclusive_end": datasets.Value("int32"),
+                                        "copy_from": datasets.Value("string"),
+                                        "copy_from_value": datasets.Sequence(datasets.Value("string")),
+                                    }
+                                ),
+                            }
+                        ),
+                        "dialogue_acts": datasets.Features(
+                            {
+                                "dialog_act": datasets.Sequence(
+                                    {
+                                        "act_type": datasets.Value("string"),
+                                        "act_slots": datasets.Sequence(
+                                            datasets.Features(
+                                                {
+                                                    "slot_name": datasets.Value("string"),
+                                                    "slot_value": datasets.Value("string"),
+                                                }
+                                            ),
+                                        ),
+                                    }
+                                ),
+                                "span_info": datasets.Sequence(
+                                    {
+                                        "act_type": datasets.Value("string"),
+                                        "act_slot_name": datasets.Value("string"),
+                                        "act_slot_value": datasets.Value("string"),
+                                        "span_start": datasets.Value("int32"),
+                                        "span_end": datasets.Value("int32"),
+                                    }
+                                ),
+                            }
+                        ),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            homepage="https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2",
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLs)
+        self.stored_dialogue_acts = json.load(open(data_files["dialogue_acts"]))
+        return [
+            datasets.SplitGenerator(
+                name=spl_enum,
+                gen_kwargs={
+                    "filepaths": data_files,
+                    "split": spl,
+                },
+            )
+            for spl, spl_enum in [
+                ("train", datasets.Split.TRAIN),
+                ("dev", datasets.Split.VALIDATION),
+                ("test", datasets.Split.TEST),
+            ]
+        ]
+    def _generate_examples(self, filepaths, split):
+        id_ = -1
+        file_list = [fpath for fname, fpath in filepaths.items() if fname.startswith(split)]
+        for filepath in file_list:
+            dialogues = json.load(open(filepath))
+            for dialogue in dialogues:
+                id_ += 1
+                mapped_acts = self.stored_dialogue_acts.get(dialogue["dialogue_id"], {})
+                res = {
+                    "dialogue_id": dialogue["dialogue_id"],
+                    "services": dialogue["services"],
+                    "turns": [
+                        {
+                            "turn_id": turn["turn_id"],
+                            "speaker": turn["speaker"],
+                            "utterance": turn["utterance"],
+                            "frames": [
+                                {
+                                    "service": frame["service"],
+                                    "state": {
+                                        "active_intent": frame["state"]["active_intent"] if "state" in frame else "",
+                                        "requested_slots": frame["state"]["requested_slots"]
+                                        if "state" in frame
+                                        else [],
+                                        "slots_values": {
+                                            "slots_values_name": [
+                                                sv_name for sv_name, sv_list in frame["state"]["slot_values"].items()
+                                            ]
+                                            if "state" in frame
+                                            else [],
+                                            "slots_values_list": [
+                                                sv_list for sv_name, sv_list in frame["state"]["slot_values"].items()
+                                            ]
+                                            if "state" in frame
+                                            else [],
+                                        },
+                                    },
+                                    "slots": [
+                                        {
+                                            "slot": slot["slot"],
+                                            "value": "" if "copy_from" in slot else slot["value"],
+                                            "start": slot.get("exclusive_end", -1),
+                                            "exclusive_end": slot.get("start", -1),
+                                            "copy_from": slot.get("copy_from", ""),
+                                            "copy_from_value": slot["value"] if "copy_from" in slot else [],
+                                        }
+                                        for slot in frame["slots"]
+                                    ],
+                                }
+                                for frame in turn["frames"]
+                                if (
+                                    "active_only" not in self.config.name
+                                    or frame.get("state", {}).get("active_intent", "NONE") != "NONE"
+                                )
+                            ],
+                            "dialogue_acts": {
+                                "dialog_act": [
+                                    {
+                                        "act_type": act_type,
+                                        "act_slots": {
+                                            "slot_name": [sl_val for sl_name, sl_val in dialog_act],
+                                            "slot_value": [sl_name for sl_name, sl_val in dialog_act],
+                                        },
+                                    }
+                                    for act_type, dialog_act in mapped_acts.get(turn["turn_id"], {})
+                                    .get("dialog_act", {})
+                                    .items()
+                                ],
+                                "span_info": [
+                                    {
+                                        "act_type": span_info[0],
+                                        "act_slot_name": span_info[1],
+                                        "act_slot_value": span_info[2],
+                                        "span_start": span_info[3],
+                                        "span_end": span_info[4],
+                                    }
+                                    for span_info in mapped_acts.get(turn["turn_id"], {}).get("span_info", [])
+                                ],
+                            },
+                        }
+                        for turn in dialogue["turns"]
+                    ],
+                }
+                yield id_, res