Data for the paper "Modeling Bilingual Conversational Characteristics for Neural Chat Translation" by the authors "Yunlong Liang, Fandong Meng, Yufeng Chen, Jinan Xu and Jie Zhou" (accepted as long paper at ACL-IJCNLP 2021 main conference)
Code will be released soon.
In this paper, we introduced a bilingual Chinese-English chat translation corpus (BMELD).
This file contains the train, dev, and test sets of the BMELD corpus It is based on the MELD corpus[1] which includes monolingual (i.e. English) dialogs.
Each line includes:
Sr No. Serial numbers of the utterances mainly for referencing the utterances in case of different versions or multiple copies with different subsets
Utterance: Individual utterances from EmotionLines as a string.
Speaker: Name of the speaker associated with the utterance.
Emotion: The emotion (neutral, joy, sadness, anger, surprise, fear, disgust) expressed by the speaker in the utterance.
Sentiment: The sentiment (positive, neutral, negative) expressed by the speaker in the utterance.
Dialogue_ID: The index of the dialogue starting from 0.
Utterance_ID: The index of the particular utterance in the dialogue starting from 0.
Season: The season no. of Friends TV Show to which a particular utterance belongs.
Episode: The episode no. of Friends TV Show in a particular season to which the utterance belongs.
StartTime: The starting time of the utterance in the given episode in the format 'hh:mm:ss,ms'.
EndTime: The ending time of the utterance in the given episode in the format 'hh:mm:ss,ms'.
Target: The Chinese translation of the corresponding English Utterance annotated by human.
Note: Following the annotation of BConTrasT[2], we assume 50% speakers speaking Chinese to keep data balance, therefore, the source and target text might be in English or Chinese depending on the role.
BMELD is based on the dialogue dataset: MELD. It is a multimodal emotionLines dialogue dataset, each utterance of which corresponds to a video, voice, and text, and is annotated with detailed emotion and sentiment. Based on MELD, we firstly crawled the corresponding Chinese translations from this and then manually post-edited them according to the dialogue history by native Chinese speakers, who are post-graduate students majoring in English. Finally, following the BConTrasT of WMT20 chat translation, we assume 50% speakers as Chinese speakers to keep data balance for Chinese-English translations and build the Bilingual MELD (BMELD). For the Chinese, we segment the sentence using Stanford CoreNLP toolkit.
[1] Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea. 2019. MELD: A multimodal multi-party dataset for emotion recognition in conversations. In Proceedings of ACL, pages 527–536.
[2] M. Amin Farajian, Ant ́onio V. Lopes, Andr ́e F. T. Martins, Sameen Maruf, and Gholamreza Haffari. 2020. Findings of the WMT 2020 shared task on chat translation. In Proceedings of WMT, pages 65–75.