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ConvAI2 Dataset
- library.toponeai.link
- paperswithcode.com
Updated Jun 14, 2022
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EmailClick to copy linkLink copiedCiteEmily Dinan; Varvara Logacheva; Valentin Malykh; Alexander Miller; Kurt Shuster; Jack Urbanek; Douwe Kiela; Arthur Szlam; Iulian Serban; Ryan Lowe; Shrimai Prabhumoye; Alan W. black; Alexander Rudnicky; Jason Williams; Joelle Pineau; Mikhail Burtsev; Jason Weston (2022). ConvAI2 Dataset [Dataset]. https://library.toponeai.link/dataset/convai2Dataset updatedJun 14, 2022AuthorsEmily Dinan; Varvara Logacheva; Valentin Malykh; Alexander Miller; Kurt Shuster; Jack Urbanek; Douwe Kiela; Arthur Szlam; Iulian Serban; Ryan Lowe; Shrimai Prabhumoye; Alan W. black; Alexander Rudnicky; Jason Williams; Joelle Pineau; Mikhail Burtsev; Jason WestonDescriptionThe ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.
To avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”
The training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively.
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ConvAI2 (Conversational Intelligence Challenge 2)
- opendatalab.com
zipUpdated Apr 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMcGill University (2023). ConvAI2 (Conversational Intelligence Challenge 2) [Dataset]. https://opendatalab.com/OpenDataLab/ConvAI2zipAvailable download formatsDataset updatedApr 1, 2023Dataset provided byMcGill University
Moscow Institute of Physics and Technology
Microsoft Research
Carnegie Mellon University
Facebook AI Research
University of MontrealDescriptionThe ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers. To avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.” The training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively.
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FacebookTwitterEmailThe ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers.
To avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.”
The training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively.