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Dialogue Commonsense Graph in Japanese
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nlp-waseda/dcsg-ja
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We proposed a commonsense knowledge graph (CSKG) grounded on dialogues.This repository contains the graph built in Japanese.
We defined the eight relations based on categories, time series, and target persons, referring toCICERO.
| Relation | Category | Time | Person |
|---|---|---|---|
| Cause | Event | Before | Speaker |
| Prerequisite | Event | Before | Listener |
| Subsequent event | Event | After | Speaker |
| Effect | Event | After | Listener |
| Motivation | Mental state | Before | Speaker |
| Guess | Mental state | Before | Listener |
| Reflection | Mental state | After | Speaker |
| Reaction | Mental state | After | Listener |
Each utterance is annotated with inferences of the eight relations, and each dialogue is stored as a list of the utterances.An example of the annotated utterances is shown below:
{"status_id":1557164592392183808,"cause": ["暑い暑い" ],"prerequisite": ["確かに全然外に出てない" ],"subsequent_event": ["宿題が終わらない" ],"effect": ["じゃあ家で宿題すればちょうどいいじゃん","外に出ないでエアコンが効いた部屋で過ごす" ],"motivation": ["暑すぎてキツイ","夏が終わらないと困る" ],"guess": ["暑くない","夏がすぐ終わると宿題が終わらなくて困る" ],"reflection": ["暑さには何も勝てないよ" ],"reaction": ["そうかもね","その通りだ" ]}Note that the utterance texts are replaced by their Twitter IDs.
We annotated 352 dialogues, corresponding to 2,121 utterances, with the inferences.The statistics of the graph is shown below:
| Relation | # triples | Avg # infs / utt |
|---|---|---|
| Cause | 3,060 | 1.44 |
| Prerequisite | 2,728 | 1.29 |
| Subsequent event | 3,001 | 1.41 |
| Effect | 3,276 | 1.54 |
| Motivation | 3,567 | 1.68 |
| Guess | 1,679 | 0.79 |
| Reflection | 1,591 | 0.75 |
| Reaction | 3,564 | 1.68 |
@InProceedings{ide_nlp2023_dcsg,author ="井手竜也 and 榮田亮真 and 河原大輔 and 山崎天 and 李聖哲 and 新里顕大 and 佐藤敏紀",title ="対話に基づく常識知識グラフの構築と対話応答生成に対する適用",booktitle ="言語処理学会第29回年次大会",year ="2023",url ="https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/H1-4.pdf" note="in Japanese"}
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Dialogue Commonsense Graph in Japanese
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