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MIntRec2.0 is a large-scale multimodal multi-party benchmark dataset for intent recognition and out-of-scope detection in conversations. We uploaded the data set MIntRec2.0 in two folders: data and data01 respectively.
Dataset Features
a.Large in Scale
Compared with our first version of multimodal intent recognition dataset (MIntRec), MIntRec2.0 increase the data-scale from 2.2K to 15K, with 30 intent classes, 9.3K in-scope and 5.7K out-of-scope annotated utterances with text, video, and audio modalities.
b.Multi-turn & Multi-party Dialogues
It contains 1,245 dialogues with an average of 12 utterances per dialogue in continuous conversations. Each utterance has an intent label in each dialogue. Each dialogue has at least two different speakers with annotated speaker identities for each utterance.
c.Out-of-scope Detection
As real-world dialogues are in the open-world scenarios as suggested in TEXTOIR, we further include an OOS tag for detecting those utterances that do not belong to any of existing intent classes. They can be used for out-of-distribution detection and improve system robustness.
Dataset Description
a.Data sources
The raw videos are collected from three TV series: Superstore, The Big Bang Theory, and Friends.
b.Dialogue division
We manually divide dialogues based on the scenes and episode.
c.Speaker information
We manually annotate 21, 7, 6 main characters in Superstore, The Big Bang Theory, and Friends, respectively.
d.Intent classes
Express emotions or attitudes (16): doubt, acknowledge, refuse, warn, emphasize, complain, praise, apologize, thank, criticize, care, agree, oppose, taunt, flaunt, joke Acheve goals (14): ask for opinions, confirm, explain, invite, plan, inform, advise, arrange, introduce, comfort, leave, prevent, greet, ask for help
Statistics
Where to send questions or comments about the model: https://github.com/thuiar/MIntRec2.0
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