Chinese-English Parallel Corpus

README.md

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+# MAPC
+
+MAPC is a manually aligned parallel corpus of Chinese-English literary texts, consisting of chapters sampled from six Chinese novels and their English translations.
+
+Although MAPC is initially created for evaluating the performance of automatic sentence aligners such as [Hunalign](http://mokk.bme.hu/en/resources/hunalign/), [Belualign](https://github.com/rsennrich/Bleualign), [Vecalign](https://github.com/thompsonb/vecalign) and [Bertalign](https://github.com/bfsujason/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 MAPC 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 [MAPC-Dev](./dev). The remaining chapters make up [MAPC-Test](./test).
+
+Please check  [Dev-metadata](./dev/meta_data.tsv) and [Test-metadata](./test/meta_data.tsv) for more information about the development and test set.
+
+##### Table 1. Makeup of MAPC
+
+| 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 MAPC
+
+| Data     | # Src_Sents | # Tgt_Sents | # Src_Tokens | # Tgt_Tokens |
+| -------- |:----------- | ----------- | ------------ | ------------ |
+| MAC-Dev  | 1,391       | 2,505       | 37,024       | 47,959       |
+| MAC-Test | 4,875       | 6,610       | 91,971       | 121,306      |
+
+## Manual Alignment and Inter-Coder Agreement
+
+The bilingual texts in MAPC are split into sentences and aligned at the sentence level using the manual alignment tool [InterText](https://wanthalf.saga.cz/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](./test/intertext_01) for the first annotator and [intertext_02](./test/intertext_02) for the second annotator.
+
+We use the Python script [mark_disagreement.py](./mark_disagreement.py) to mark up any differences between two annotators:
+
+```bash
+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](./img/fig_1.JPG)
+
+##### 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 fluent and adequate.
+
+All the cases of annotator differences and the corresponding resolutions have been recorded in an Excel file [anno_disagreement.xlsx](./test/anno_disagreement.xlsx). The final alignments verified by both annotators are saved in the directory [dev/Intertext](./dev/Intertext) and [test/Intertext](./test/Intertext).
+
+We use the set-based metric Jaccard Index as suggested by Artstein & Poesio (2008) to measure the Inter-Coder Agreement (ICA):
+
+```bash
+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](./intertext2tsv.py) to convert Intertext XML files into TSV format:
+
+```bash
+python intertext2tsv.py -i test/intertext -o test/tsv
+```
+
+The bilingual corpus in TSV format will be saved in the [tsv directory](./test/tsv).
+
+## References
+
+Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. *Computational linguistics*, *34*(4), 555-596.