The dataset viewer is not available for this split.
Error code: FeaturesError Exception: ArrowInvalid Message: JSON parse error: Column() changed from object to array in row 0 Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 145, in _generate_tables dataset = json.load(f) File "/usr/local/lib/python3.9/json/__init__.py", line 293, in load return loads(fp.read(), File "/usr/local/lib/python3.9/json/__init__.py", line 346, in loads return _default_decoder.decode(s) File "/usr/local/lib/python3.9/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 1072) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__ yield from islice(self.ex_iterable, self.n) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__ for key, pa_table in self.generate_tables_fn(**self.kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 148, in _generate_tables raise e File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
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Enriched Topical-Chat: A Dialogue Act and Knowledge Sentence annotated version of Topical-Chat
This README describes Enriched Topical-Chat, an augmentation of Topical-Chat that contains dialogue act and knowledge sentence annotations for each turn in the dataset. Each annotation is automatically annotated using off-the-shelf models.
Knowledge Sentence Annoations
Each conversation in Topical-Chat has a pair of reading sets which consists of a set of knowledge sentences. For every turn and knowledge sentence in the Topical-Chat dataset, we computed a TFIDF vector. We then computed the cosine similarity between a turn in the conversation and selected the top-1 knowledge sentence. In the new data release, for each conversation turn, we will present the knowledge sentences selected along with the similarity score.
Dialogue Act Annoations
We obtain the dialogue acts for each turn by running an off-the-shelf SVM dialogue act tagger released by (https://github.com/ColingPaper2018/DialogueAct-Tagger). This tagger was trained on five datasets (Switchboard, Oasis BT, Maptask, VerbMobil2, AMI).
Prerequisites
After cloning the repo you must first follow the instructions in https://github.com/alexa/Topical-Chat/blob/master/README.md. This includes building the original dataset along with the reading sets. Look at the Build Section for the exact steps. Once the original dataset is built each .json file will contain pointers to the knowledge sentences in the reading sets.
Conversations:
The data is hosted on s3. To pull the data run these commands:
wget https://enriched-topical-chat.s3.amazonaws.com/train.json
wget https://enriched-topical-chat.s3.amazonaws.com/valid_freq.json
wget https://enriched-topical-chat.s3.amazonaws.com/valid_rare.json
wget https://enriched-topical-chat.s3.amazonaws.com/test_freq.json
wget https://enriched-topical-chat.s3.amazonaws.com/test_rare.json
Each .json file has the specified format:
{
"t_d004c097-424d-45d4-8f91-833d85c2da31": {
"article_url": "<link to washington post article>",
"config": "C",
"content": [
{
"message": ["Did you know that the University of Iowa's locker room is
painted pink?", "I wonder why?"],
"agent": "agent_1",
"segmented_annotations": [
{
"da": "PropQ",
"gt_ks": {"score": 0.73,"ds": "wiki",
"section": "FS1", "start_index": 0, "end_index": 100},
},
{
"da": "ChoiceQ",
"gt_ks": {"score": 0.0, "ds": "article", "section": "AS4",
"start_index": 0, "end_index": 100},
}
],
"gt_turn_ks": {"score": 0.67, "ds": "fun_facts",
"section": "FS1", "index": 0}
},
The additional fields are:
message: a list containing the segments of each turn
segmented_annotations: a list of annotations for each segment within a turn each.
da: ground truth dialog act associated with segmented response
gt_ks: ground truth knowledge sentence associated with segmented response
ds: data source knowledge retrieved from. wiki, fun_facts or article
fun_facts:
section: which section containing the fun facts i.e. FS1
index: which element in the list of fun facts
wiki:
section: which section containing the wikipedia sentence i.e. FS2
start_index: index of beginning character of sentence in article
end_index: index of end character
article:
section: which section of article. i.e. AS4
start_index: index of beginning character of sentence in article
end_index: index of end character
gt_turn_ks: ground truth knowlege sentence associated with turn
Citation
Enriched Topical-Chat
@article{hedayatnia2020policy,
title={Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems},
author={Hedayatnia, Behnam and Gopalakrishnan, Karthik and Kim, Seokhwan and Liu, Yang and Eric, Mihail and Hakkani-Tur, Dilek},
journal={arXiv preprint arXiv:2005.12529},
year={2020}
}
Topical-Chat
@inproceedings{gopalakrishnan2019topical,
author={Gopalakrishnan, Karthik and Hedayatnia, Behnam and Chen, Qinlang and Gottardi, Anna and Kwatra, Sanjeev and Venkatesh, Anu and Gabriel, Raefer and Hakkani-Tür, Dilek},
title={{Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations}},
year={2019},
booktitle={INTERSPEECH}
}
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