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MAC

MAC is a manually aligned Chinese-English parallel corpus of literary texts, consisting of chapters sampled from six Chinese novels and their English translations.

Although MAC is initially created for evaluating the performance of automatic sentence aligners such as Gale-Church, Hunalign, Belualign, Vecalign and Bertalign, the corpus can also be used in the study of contrastive linguistics, the difference between translated v.s. non-translated language and translation strategies, etc.

Makeup and Statistics

The novels in MAC are selected from a range of different genres and for each novel, five chapters are sampled from the beginning, middle and end of the book. We then take one chapter from each novel and construct a development set MAC-Dev. The remaining chapters make up MAC-Test.

Please check Dev-metadata and Test-metadata for more information about the development and test set.

Table 1. Makeup of MAC
Genre Book_Title Author Trans_Title Translator
Humor 黄金时代 王小波 The Golden Age Hongling Zhang; Jason Sommer
Martial Arts 鹿鼎记 金庸 The Deer and The Cauldron John Minford
Classic 红楼梦 曹雪芹 The Story of the Stone David Hawkes
War 红高粱 莫言 Red Sorghum Howard Goldblatt
Romance 长恨歌 王安忆 The Song of Everlasting Sorrow Michael Berry; Susan Chan Egan
Sci-fi 三体 刘慈欣 The Three-Body Problem Ken Liu
Table 2. Statistics of MAC
Data # Src_Sents # Tgt_Sents # Src_Tokens # Tgt_Tokens # 1-1 (%)
MAC-Dev 1,444 1,947 21,911 31,374 817 (61.5)
MAC-Test 4,799 6,573 73,635 105,407 2,628 (59.8)
Total 6,243 8,520 95,546 136,781 3,445 (60.2)

Manual Alignment and Inter-Coder Agreement

The bilingual texts in MAC are split into sentences and aligned at sentence level using the manual alignment tool InterText.

The manual alignment was made by two annotators to ensure accuracy and reliability. The hand-checked alignments are saved in the directory intertext_01 for the first annotator and intertext_02 for the second annotator.

We use the Python script mark_disagreement.py to mark up any differences between two annotators:

python mark_disagreement.py -a1 test/intertext_01/test-anno-1.001_zh.001_en.xml -a2 test/intertext_02/test-anno-2.001_zh.001_en.xml

anno_diff

Figure 1. Markup of Annotator Differences

The observed differences are then resolved through discussions between the annotators. We found that many disagreements can be attributed to various translation techniques (e.g., omission, addition and sentence inversion) employed by translators to make the target texts more adequate and fluent.

All the cases of annotator differences and the corresponding resolutions have been recorded in an Excel file anno_disagreement.xlsx. The final alignments verified by both annotators are saved in the directory dev/Intertext and test/Intertext.

We use the script compute_ica.py, which implements the set-based metric Jaccard Index as suggested by Artstein & Poesio (2008), to measure the Inter-Coder Agreement (ICA):

python compute_ica.py -a1 test/intertext_01 -a2 test/intertext_02

TSV Format

To facilitate follow-up search and annotation of parallel corpus, you can run the Python script intertext2tsv.py to convert Intertext XML files into TSV format:

python intertext2tsv.py -i test/intertext -o test/tsv

The bilingual corpus in TSV format will be saved in the tsv directory.

References

Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational linguistics, 34(4), 555-596.