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# Bertalign
word embedding-based bilingual sentence aligner
Word Embedding-Based Bilingual Sentence Aligner
## Evaluation Corpus
This section describes the procedure of creating the evaluation corpora: the manually aligned corpus (MAC) of Chinese-English literary texts and the Bible corpus aligned at the verse level.
### MAC
Firstly, 5 chapters and their translations are sampled from each of the 6 novels included in MAC, obtaining a corpus of 30 bitexts. We then split the corpus into **MAC-Dev** and **MAC-Test** with the former containing 6 chapters and the latter 24 chapters.
Firstly, 5 chapters and their translations are sampled from each of the 6 novels included in MAC, obtaining a corpus of 30 bitexts. We then split the corpus into MAC-Dev and MAC-Test with the former containing 6 chapters and the latter 24 chapters.
The **MAC-Test** is saved in [corpus/mac/test](./corpus/mac/test)
The sampling schemes for building **MAC-Test** can be found at [corpus/mac/test/meta_data.tsv](./corpus/mac/test/meta_data.tsv)
The sampling schemes for building MAC-Test can be found at [meta_data.tsv](./corpus/mac/test/meta_data.tsv)
There are 4 subdirectories in **MAC-Test**. The [split](/corpus/mac/test/split) directory contains the sentence-split source texts, target texts and the machine translations of source texts, which are required by **Bleualign** to perform automatic alignment.
There are 4 subdirectories in MAC-Test.
The inputs to **Hunalign** are saved in the [tok](/corpus/mac/test/tok) directory.
The [split](./corpus/mac/test/split) directory contains the sentence-split source texts, target texts and the machine translations of source texts, which are required by *Bleualign* to perform automatic alignment.
The emb directory is made up of the overlapping sentences and their embeddings for Vecalign and BertAlign.
The inputs to *Hunalign* are saved in the [tok](./corpus/mac/test/tok) directory.
The [emb](./corpus/mac/test/emb) directory is made up of the overlapping sentences and their embeddings for *Vecalign* and *BertAlign*.
We use [Intertext](https://wanthalf.saga.cz/intertext) to perform the manual alignment for MAC and save the gold alignments in the [intertext](./corpus/mac/test/intertext) directory.
In order to facilitate system evaluations, we delete the XML tags and save the clean gold alignment file with only sentence IDs in the [gold](./eval/mac/test/gold) directory
### Bible
The Bible corpus is located in [corpus/bible](./corpus/bible)
The directory makeup is similar to MAC, except that there is no *intertext* directory for manual alignments.
The gold alignments for the Bible corpus are generated automatically from the original verse-aligned Bible corpus and saved in [eval/bible/gold](./eval/bible/gold)
In order to compare the sentence-based alignments returned by various aligners with the verse-based gold alignments, we put the verse ID for each sentence in the files *corpus/bible/en.verse* and *corpus/bible/zh.verse*, which are used to merge consecutive sentences in the output if they belong to the same verse.
## System Comparisons
All the experiments reported in the paper are conducted using [Google Colab](https://colab.research.google.com/)
### Job File
Before performing the automatic alignment, a job file is created for each aligner for batch processing. Each row in the job file represents an alignment task, which is made of three tab-separated file names for source, target and output text.
The job files for MAC-Test and Bible are located in *eval/mac/test/job* and *eval/bible/job*
### Sentence Embeddings
Before embedding the source and target sentences, we use the following Python script to create the combinations of consecutive sentences:
```
# MAC-Test
python utils/overlap.py -i corpus/mac/test/split -o corpus/mac/test/emb/en.overlap –l en –n 8
python utils/overlap.py -i corpus/mac/test/split -o corpus/mac/test/emb/zh.overlap –l zh –n 8
# Bible
python utils/overlap.py -i corpus/bible/split -o corpus/bible/en.overlap –l en –n 5
python utils/overlap.py -i corpus/bible/split -o corpus/bible/zh.overlap –l en –n 5
```
Use parameters -i to specify the input data directory and -o the output file path.
All the file suffixes in the input directory should end with the corresponding language code, e.g. 001.en and 001.zh etc., and match up with the parameter -l.
The parameter -n indicates the number of overlapping sentences, which is similar to word n-grams applied to sentences.
We use [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) to convert texts into embeddings.
To install Sentence Transformers, just run:
```
pip install sentence-transformers
```
After the installation, we run the following Python script to embed the bitexts to be aligned:
```
# MAC-Test
python utils/embed.py –i corpus/mac/test/emb/en.overlap –o corpus/mac/test/emb/en.overlap.emb
python utils/embed.py –i corpus/mac/test/emb/zh.overlap –o corpus/mac/test/emb/zh.overlap.emb
# Bible
python utils/embed.py –i corpus/bible/emb/en.overlap –o corpus/bible/emb/en.overlap.emb
python utils/embed.py –i corpus/bible/emb/zh.overlap –o corpus/bible/emb/zh.overlap.emb
```
The parameter -i indicates the file containing sentence combinations.
We use the [tofile](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html) method provided by Python’s Numpy module to save the sentence embeddings in the file designated by -o.