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TED-Q: TED Talks and the Questions they Evoke

Contents of this repository

See further below for an explanation of the structure of these .csv files.

  • TED-Q_elicitation.csv: Data from our elicitation phase: evoked questions and their (non-)answers.
  • TED-Q_comparison_raw.csv: Data from our comparison phase: how related are the evoked questions to each other -- individual annotator's judgments per question pair.
  • TED-Q_comparison_aggregated.csv: Data from our comparison phase, aggregated annotator's judgments per question pair (mean).

Download the source texts

TED-Q provides an additional layer of annotations to the existing TED-MDB dataset. The source texts are not included in the current repository; download them here:

Or here (forked):


If you use this resource, please cite our LREC paper:

  title={TED-Q: TED Talks and the Questions they Evoke},
  author={Matthijs Westera and Laia Mayol and Hannah Rohde},
  booktitle = "Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC'2020)",
  year = 	 "2020",
  month = 	 "May",
  date =     "13-15",
  address =  "Marseille, France",
  publisher = "European Language Resource Association (ELRA)",

And consider citing also the authors of the TED-MDB dataset, whose source texts we used:

  title={TED Multilingual Discourse Bank (TED-MDB): a parallel corpus annotated in the PDTB style},
  author={Zeyrek, Deniz and Mendes, Amalia and Grishina, Yulia and Kurfali, Murathan and Gibbon, Samuel and Ogrodniczuk,    Maciej},
  journal={Language Resources and Evaluation},

  title={Multilingual Extension of PDTB-Style Annotation: The Case of TED Multilingual Discourse Bank.},
  author={Zeyrek, Deniz and Mendes, Amalia and Kurfali, Murathan},

Structure of the .csv files


  • excerpt_number: the number of excerpts (up to 6) this annotator has seen including the current one.

  • chunk_number: the number of chunks (up to 8) this annotator has seen within this excerpt, including the current one.

  • worker: a made-up name uniquely identifying the annotator.

  • type: the type of annotation, among 'question', 'answer' ('answered' score >= 3), 'non-answer' ('answered' score <= 2), or 'evaluation' (some meta-questions at the end of each fragment).

  • content (for 'question'/'answer' type annotations only): the question/answer as formulated by the annotator in their own words.

  • answered: for 'answer'/'non-answer' type annotations, the degree to which it provided an answer to the given question ('prior_question'); for 'question' type annotations, the maximal degree to which it was answered.

  • highlight (for 'question'/'answer') type annotations only): the words selected by the person as either triggering the question or providing the answer.

  • prior_question (for 'answer'/'non-answer' type annotations only): annotation id of the question to which the current chunk provides a (non-)answer.

  • best_answer (for 'question' type annotations only): annotation id of its best answer.

  • coherence/naturalness/comment (for 'evaluation' type annotations only): after every fragment (around 8 chunks per fragment) we asked participants whether the text was coherent and natural (scales from 1 to 5), and provided an open text field for comments.

  • relatedness (for 'question' type annotations only): how related a question is, on average, to other questions elicited by the same chunk (according to aggregated verification data).

  • source: identifier of the source text, assuming the directory structure in the Ted-MDB-Annotations github repo (see URL above).

  • chunk_start/chunk_end: the start/end position (by number of characters) in the source text of the chunk (two sentences) presented to the annotator when eliciting the annotation.

  • highlight_start/highlight_end (for 'question'/'answer' type annotations only): the start/end position (by number of characters) in the source text of the phrase highlighted by the annotator (depending on 'type': the trigger of the question, or the part providing the answer)


We asked annotators to judge how related two questions were given the context that evoked them.

  • workerid: anonymized identifier of the annotator

  • snippet: presented to annotators for judging question relatedness in context, roughly two sentences from the source text, including the chunk that evoked the questions to be judged.

  • target_question: the target question

  • comparison_question: the question which they were asked to compare to the target question

  • relatedness: how related they judged the two questions to be, from 0 (not closely related) to 3 (equivalent)

  • target_question_id: annotation id of the target question (for linking to elicitation data)

  • comparison_question_id: annotation id of the comparison question (for linking to elicitation data)


We aggregated relatedness judgments by taking the mean, conflating target/comparison pairs in either order (making for ~6 judgments per pair):

  • question1_id / question2_id: annotation id of the questions (for linking to elicitation data).

  • snippet: the snippet of text against which question relatedness was judged (as above).

  • question1 / question2: the questions in plain text.

  • relatedness_mean: mean of the individual judgments for this pair.

  • relatedness_count: how many individual judgments for this pair.

  • relatedness_list: list containing the individual judgments for this pair.

  • relatedness_std: standard deviation among the individual judgments for this pair.


Repository for the TED-Q dataset: TED Talks and the Questions they Evoke.






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