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DIRECT: Direct and Indirect REsponses in Conversational Text Corpus

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DIRECT: Direct and Indirect REsponses in Conversational Text Corpus

We create a large-scale dialogue corpus that provides pragmatic paraphrases to advance technology for understanding users' nonverbal intentions.
Our corpus provides a total of 71,498 indirect-direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset.

train.csv consists of five columns except for the index column.

column name description
dialogue_id The corresponding dialogue ID in the MultiWoZ data
turn_index The position of the utterance in the dialogue (0-based index)
target_utterance The original utterance extracted from the MultiWoZ data
direct_utterance Collected direct response
indirect_utterance Collected indirect responses

In addition to the above columns, test.csv contains the quality evaluation results.

column name description
isacceptable_direct Whether direct_utterance is considered to be the same intention as target_utterance (TRUE / FALSE)
isacceptable_indirect Whether indirect_utterance is considered to be the same intention as target_utterance (TRUE / FALSE)
quality Whether direct_utterance is more direct than indirect_utterance (Good / Neutral / Bad)

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