XLM

XLM stands for “Cross-lingual Language Modeling” which is a model created by FacebookAI in 2019 and published in this paper: “Cross-lingual Language Model Pretraining”. XLM is an 12-layer encoder-transformer of with 1024 hidden units, 16 heads, and GELU activation.

XLM was trained on multiple languages with new pre-training objectives that pushed the XNLI benchmark by an absolute gain of 4.9% accuracy and obtained 34.3 BLEU on WMT’16 German-English which is 9 BELU more than the state-of-the-art back then. The official code for this paper can be found in the FairSeq official repository: fairseq/cross_lingual_language_model.

In this paper, the authors presented three language modeling objectives for the language modeling. Two of them only require monolingual data (unsupervised), while the third one requires parallel sentences (supervised). And these objectives are:

• CLM (Causal Language Modeling).

• MLM (Masked Language Modeling).

• TLM (Translation Language Modeling).

Before getting into pre-training, let’s first check the vocabulary used for our XLM.

Shared Vocabulary

In the paper, they processed all languages with the same shared vocabulary created through Byte Pair Encoding (BPE). They used 80k BPE splits and a vocabulary of 95k. This greatly improves the alignment of embedding spaces across languages that share either the same alphabet or other tokens such as digits or proper nouns.

They learned the BPE splits on the concatenation of sentences sampled randomly from the monolingual corpora. Sentences are sampled according to a multinomial distribution with probabilities $\left[ q_i \right]_{i = 1..N}$, where $N$ is the number of languages and $n_{i}$ is the number of sentences in the $i^{th}$ monolingual corpora:

Sampling with this distribution increases the number of tokens associated to low-resource languages and alleviates the bias towards high-resource languages. In particular, this prevents words of low-resource languages from being split at the character level.

Pre-training Objectives

In this part, we are going to discuss the three language modeling objectives used for the language modeling pre-training. The first two of them only require monolingual data (unsupervised), while the third one requires parallel sentences (supervised).

CLM

CLM stands for “Causal Language Modeling” which is one of the three objectives used for learning the cross-lingual language model. This objective is used to train the model predict the probability of a word given the previous words in a sentence $P\left( w_{t} \middle| w_{1},\ …\ w_{t - 1},\ \theta \right)$. OpenAI’s GPT and GPT-2 are trained on this objective. You can refer to my articles on GPT and GPT-2 if you’re interested in the details of this objective.

MLM

MLM stands for “Masked Language Modeling” which is the second objective out of the three objectives used for learning the cross-lingual language model. MLM is the same objective used for BERT where we randomly sample 15% of the BPE tokens from the text streams, replace them by a [MASK] token 80% of the time, by a random token 10% of the time, and we keep them unchanged 10% of the time.

In face, there are two differences between this approach and the one used with BERT. And they are:

• Here, they used text streams of an arbitrary number of sentences (truncated at 256 tokens) instead of pairs of sentences.

• To counter the imbalance between frequent tokens (e.g. punctuations or stop words) and rare tokens, they sub-sampled the frequent outputs according to a multinomial distribution, whose weights are proportional to the square root of their invert frequencies.

TLM

TLM stands for “Translation Language Modeling” which is the third and last objective used for learning the cross-lingual language model. Both the CLM and MLM objectives are unsupervised and only require monolingual data. However, these objectives cannot be used to leverage parallel data when it is available. So, this objective is used to leverage the existence of parallel data to improve cross-lingual pre-training.

TLM is an extension of MLM, where instead of considering monolingual text streams, we concatenate parallel sentences and perform MLM over this concatenation. For example, let’s assume that we have a parallel entry of two languages English and French as shown below:

To predict a word masked in an English sentence, the model can either attend to surrounding English words or to the French translation, encouraging the model to align the English and French representations. In particular, the model can leverage the French context if the English one is not sufficient to infer the masked English words. To facilitate the alignment, we also reset the positions of target sentences.

Fine-tuning

In the paper, the XLM model was fine-tuned on various tasks -all from the XNLI dataset- which shows that XLM can be used for a wide variety of cross-lingual tasks:

• Zero-shot Cross-lingual Classification:
  They fine-tuned XLMs on a cross-lingual classification by adding a linear classifier on top of the first hidden state of the pretrained Transformer, and fine-tuned all parameters on the English NLI training dataset and evaluated it on the other 15 XNLI languages:

• Unsupervised Machine Translation:
  They pretrained the entire encoder and decoder with a cross-lingual language model and explored various initialization schemes as shown below:

• Low-resource Language Modeling:
  Here’s where “languages with the same script or similar words provide better mapping” comes into the picture. For example, around 80% of Nepali tokens (low-resource language) is in common with Hindi (high-resource-language):

• Cross-lingual Word Embedding:
  Since we have a shared vocabulary, the lookup table (or embedding matrix) of the XLM model gives us the cross-lingual word embeddings.