The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    FileNotFoundError
Message:      https://ixa2.si.ehu.es/convai/doqa-v2.1.zip
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/connector.py", line 992, in _wrap_create_connection
                  return await self._loop.create_connection(*args, **kwargs)
                File "/usr/local/lib/python3.9/asyncio/base_events.py", line 1090, in create_connection
                  transport, protocol = await self._create_connection_transport(
                File "/usr/local/lib/python3.9/asyncio/base_events.py", line 1120, in _create_connection_transport
                  await waiter
                File "/usr/local/lib/python3.9/asyncio/sslproto.py", line 534, in data_received
                  ssldata, appdata = self._sslpipe.feed_ssldata(data)
                File "/usr/local/lib/python3.9/asyncio/sslproto.py", line 188, in feed_ssldata
                  self._sslobj.do_handshake()
                File "/usr/local/lib/python3.9/ssl.py", line 945, in do_handshake
                  self._sslobj.do_handshake()
              ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1129)
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 414, in _info
                  await _file_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 845, in _file_info
                  r = await session.get(url, allow_redirects=ar, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client.py", line 578, in _request
                  conn = await self._connector.connect(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/connector.py", line 544, in connect
                  proto = await self._create_connection(req, traces, timeout)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/connector.py", line 911, in _create_connection
                  _, proto = await self._create_direct_connection(req, traces, timeout)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/connector.py", line 1235, in _create_direct_connection
                  raise last_exc
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/connector.py", line 1204, in _create_direct_connection
                  transp, proto = await self._wrap_create_connection(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/connector.py", line 994, in _wrap_create_connection
                  raise ClientConnectorCertificateError(req.connection_key, exc) from exc
              aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host ixa2.si.ehu.es:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1129)')]
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 126, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 103, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1384, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 234, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/doqa/5bc0cb24c4eeb61f2004f1e0ba9b4135cd5a814770b0a042dbf0725e1d2ad1eb/doqa.py", line 158, in _generate_examples
                  with open(filepath, encoding="utf-8") as f:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 506, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 452, in open
                  out = open_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 280, in open_files
                  fs, fs_token, paths = get_fs_token_paths(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 622, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 290, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 79, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 56, in __init__
                  self.fo = fo.__enter__()  # the whole instance is a context
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 100, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1307, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 353, in _open
                  size = size or self.info(path, **kwargs)["size"]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 118, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 103, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 56, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 427, in _info
                  raise FileNotFoundError(url) from exc
              FileNotFoundError: https://ixa2.si.ehu.es/convai/doqa-v2.1.zip

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Dataset Card for "doqa"

Dataset Summary

DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues (10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often referred to as internal “knowledge bases”.

These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable questions and some relevant dialogue acts.

DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

cooking

  • Size of downloaded dataset files: 4.19 MB
  • Size of the generated dataset: 11.31 MB
  • Total amount of disk used: 15.51 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "answers": {
        "answer_start": [852],
        "text": ["CANNOTANSWER"]
    },
    "background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
    "context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
    "followup": "n",
    "id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
    "orig_answer": {
        "answer_start": [852],
        "text": ["CANNOTANSWER"]
    },
    "question": "Ok. What can I add to make it more softer and avoid hardening?",
    "title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
    "yesno": "x"
}

movies

  • Size of downloaded dataset files: 4.19 MB
  • Size of the generated dataset: 3.17 MB
  • Total amount of disk used: 7.36 MB

An example of 'test' looks as follows.

This example was too long and was cropped:

{
    "answers": {
        "answer_start": [852],
        "text": ["CANNOTANSWER"]
    },
    "background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
    "context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
    "followup": "n",
    "id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
    "orig_answer": {
        "answer_start": [852],
        "text": ["CANNOTANSWER"]
    },
    "question": "Ok. What can I add to make it more softer and avoid hardening?",
    "title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
    "yesno": "x"
}

travel

  • Size of downloaded dataset files: 4.19 MB
  • Size of the generated dataset: 3.22 MB
  • Total amount of disk used: 7.41 MB

An example of 'test' looks as follows.

This example was too long and was cropped:

{
    "answers": {
        "answer_start": [852],
        "text": ["CANNOTANSWER"]
    },
    "background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
    "context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
    "followup": "n",
    "id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
    "orig_answer": {
        "answer_start": [852],
        "text": ["CANNOTANSWER"]
    },
    "question": "Ok. What can I add to make it more softer and avoid hardening?",
    "title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
    "yesno": "x"
}

Data Fields

The data fields are the same among all splits.

cooking

  • title: a string feature.
  • background: a string feature.
  • context: a string feature.
  • question: a string feature.
  • id: a string feature.
  • answers: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.
  • followup: a string feature.
  • yesno: a string feature.
  • orig_answer: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.

movies

  • title: a string feature.
  • background: a string feature.
  • context: a string feature.
  • question: a string feature.
  • id: a string feature.
  • answers: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.
  • followup: a string feature.
  • yesno: a string feature.
  • orig_answer: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.

travel

  • title: a string feature.
  • background: a string feature.
  • context: a string feature.
  • question: a string feature.
  • id: a string feature.
  • answers: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.
  • followup: a string feature.
  • yesno: a string feature.
  • orig_answer: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.

Data Splits

cooking

train validation test
cooking 4612 911 1797

movies

test
movies 1884

travel

test
travel 1713

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

More Information Needed

Licensing Information

More Information Needed

Citation Information


@misc{campos2020doqa,
    title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
    author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
    year={2020},
    eprint={2005.01328},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contributions

Thanks to @mariamabarham, @thomwolf, @lhoestq for adding this dataset.

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