# Using Marian with factors¶

Following this README should allow the user to train a model with source and/or target side factors. To train with factors, the data must be formatted in a certain way. A special vocabulary file format is also required, and its extension should be .fsv as providing a source and/or target vocabulary file with this extension is what triggers the usage of source and/or target factors. See details below.

## Requirements:¶

In order to use factors in Marian, you should use at least Marian 1.9.0 unless you want to use a factors functionality that requires setting one of the following command line options to their non default values: --factors-combine, -—factors-dim-emb and --lemma-dependency as they were only introduced after Marian 1.10.20+.

## Define factors¶

Factors should be organized in “groups,” where each group represents a different feature. For example, there could be a group denoting capitalization and another denoting subword divisions.

For example, for a capitalization factor group, the individual factors could be:

• c0: all lowercase

• c1: first character capitalized, rest lowercase

• c2: all uppercase

If there were a second factor group for subword divisions, the individual factors could be:

• s0: end of word, whitespace should follow

• s1: join token with next subword

There is no limit on the number of factor groups barring some practical limitations having to do with how the vocabulary is stored by marian. If the limit is exceeded marian will throw an error.

Factor group zero is always the actual words in the text, referred to as lemmas.

## Data preparation¶

Factors are appended to the lemmas with a pipe |. The pipe also separates factors of multiple groups.

Example sentence:

Trump tested positive for COVID-19.


Preprocessed sentence:

trump test@@ ed positive for c@@ o@@ v@@ i@@ d - 19 .


Apply factors:

trump|c1|s0 test|c0|s1 ed|c0|s0 positive|c0|s0 for|c0|s0 c|c2|s1 o|c2|s1 v|c2|s1 i|c2|s1 d|c2|s0 -|c0|s0 19|c0|s0 .|c0|s0


## Create the factored vocabulary¶

Factored vocabularies must have the extension .fsv. How to structure the vocabulary file is described below. If using factors only on the source or target side, the vocabulary of the other side can be a normal json, yaml, etc.

The .fsv vocabulary must have two sections:

1. Factors

The factor groups are defined with an underscore prepended. The colon indicates which factor group each factor inherits from. _has_c is used in the definition of the words in the vocabulary (see #2 below) to indicate that that word has that factor group. The _lemma factor is used for the words/tokens themselves; this must be present.

_lemma

_c
c0 : _c
c1 : _c
c2 : _c
_has_c

_s
s0 : _s
s1 : _s
_has_s

2. Lemmas

These are the vocabulary entries themselves. They have the format of LEMMA : _lemma [_has_c] [_has_s]. The _has_X should only apply to lemmas that can have an X factor anywhere in the data (which will likely be all of the tokens except </s> and <unk>).

Examples:

</s> : _lemma
<unk> : _lemma
, : _lemma _has_c _has_s
. : _lemma _has_c _has_s
the : _lemma _has_c _has_s
for : _lemma _has_c _has_s


### Other Requirements¶

Certain characters are used by the .fsv vocabulary that will have to be escaped/replaced in the data: #:_\|

The tokens in the factor vocabularies (c0, c1, s0, etc.) cannot be present in any of the lemmas.

### Full .fsv file¶

Putting everything together, the final .fsv file should look like this. It can have comments (lines started by #).

# factors

_lemma

_c
c0 : _c
c1 : _c
c2 : _c
_has_c

_s
s0 : _s
s1 : _s
_has_s

# lemmas

</s> : _lemma
<unk> : _lemma
, : _lemma _has_c _has_s
. : _lemma _has_c _has_s
the : _lemma _has_c _has_s
for : _lemma _has_c _has_s


## Training options¶

There are two choices for how factor embeddings are combined with lemma embeddings: summation and concatenation.

--factors-combine TEXT=sum      How to combine the factors and lemma embeddings.
Options available: sum, concat


The dimension of the factor embeddings must be specified if using combine option concat. If using sum, the factor embedding dimension matches that of the lemmas.

--factors-dim-emb INT           Embedding dimension of the factors. Only used if concat is selected as factors combining form


Note: At the moment concat is only implemented for usage in the source side.

### Prediction¶

If using factors on the target side, there are multiple options for how factor predictions are generated related to the form of conditioning / dependencies of factors and lemmas. If no option is set with --lemma-dependency, the default behavior will be predicting the factors with no lemma dependency.

--lemma-dependency TEXT         Lemma dependency method to use when predicting target factors.
Options: soft-transformer-layer, hard-transformer-layer, lemma-dependent-bias, re-embedding

--lemma-dim-emb INT=0           Re-embedding dimension of lemma in factors

• soft-transformer-layer: Uses an additional transformer layer to predict the factors using the previously predicted lemma

• hard-transformer-layer: Like soft-transformer-layer but with hard-max

• lemma-dependent-bias: Adds a learned bias term based on the predicted lemma to the logits of the factors. There is no additional transformer layer introduced with this option

• re-embedding: After predicting a lemma, re-embed the lemma and add this new vector before predicting the factors

• lemma-dim-emb: Controls the dimension of the re-embedded lemma when using the option re-embedding

### Weight tying¶

If you use factors both on the source and target side, and the factors are the same for both sides you can tie the embeddings exactly as you do for non factored models.

If factors are used only on one side (either source or target) with a joint vocabulary, there are two options for tying source and target embedding weights:

1. Use combine option concat (If using factors only on the source side).

2. Use combine option sum, and create “dummy” factors on the non-factorized side. This entails creating a factored vocabulary where the same number of factors are present as are on the side with meaningful factors. In the previous example, if we have the capitalization and subword factors on the source side, the target side would have five different dummy factors (they can all be in the same group). In the lemma section of the .fsv file we would just not put _has_X for any lemma.

# factors

_lemma

_d
d0 : _d
d1 : _d
d2 : _d
d3 : _d
d4 : _d
_has_d

# lemmas

</s> : _lemma
<unk> : _lemma
, : _lemma
. : _lemma
le : _lemma
pour : _lemma


## Examples¶

Some examples of possible commands to train factored models in marian:

• Using factors on both source and target. Using sum to combine lemma and factor embeddings. No tied embeddings and no lemma dependency when predicting the factors:

path_to/build/marian -t corpus.fact.{src,trg} \
-v vocab.{src,trg}.fsv

• Using factors only on the source side. Using concat to combine lemma and factor embeddings. Source, target and output embeddings matrices tied:

path_to/build/marian -t corpus.fact.src corpus.trg \
-v vocab.src.fsv vocab.trg.yml \
--factors-combine concat \
--factors-dim-emb 8 \
--tied-embeddings-all

• Using factors only on the target side. Using sum to combine lemma and factor embeddings. Target and output embedding matrices tied. Predicting factors with soft-transformer-layer lemma dependency:

path_to/build/marian -t corpus.src corpus.fact.trg \
-v vocab.src.yml vocab.fsv.trg \
--tied-embeddings \
--lemma-dependency soft-transformer-layer