Phanxuan Phuc

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Published by: Google Research & Carnegie Mellon University

AMBER

AMBER stands for “Aligned Multilingual Bidirectional EncodeR” is a cross-lingual language model that adopts the same architecture as BERT; where the contextual embeddings of words/sentences with similar meanings across languages are aligned together in the same space. AMBER was proposed by Google Research in collaboration with Carnegie Mellon University in 2020 and published in their paper: Explicit Alignment Objectives for Multilingual Bidirectional Encoders. The official code for this paper can be found in this GitHub repository: amber.

Cross-lingual Alignment

To produce language-independent representations, AMBER was trained on monolingual and parallel data using three alignment objectives that align the multilingual word/sentence representations together. These three alignment objectives are:

  • MLM or TLM:
    This objective, proposed in BERT, takes a pair of sentences $\left( x,y \right)$, and optimizes the prediction of randomly masked tokens in the concatenation of the sentence pair $\left\lbrack x;y \right\rbrack$. When $x$ and $y$ are in the same language, it’s Masked Language Modeling (MLM). When they are in two different languages, it’s Translation Language Modeling (TLM). This can be described as follows where $\left\lbrack x;y \right\rbrack_{s}$ is the masked tokens of the concatenation while $\left\lbrack x;y \right\rbrack_{\backslash s}$ is the unmasked tokens:
\[\mathcal{L}_{\text{MLM}}\left( x,y \right) = - \mathbb{E}_{s\sim\left\lbrack 1,\left| \left\lbrack x;y \right\rbrack \right| \right\rbrack}\log\text{ P}\left( \left\lbrack x;y \right\rbrack_{s} \middle| \left\lbrack x;y \right\rbrack_{\backslash s} \right)\]
  • Sentence Alignment:
    This objective encourages cross-lingual alignment of sentence representations. Given sentence pair $\left( x,y \right)$, we separately calculate sentence embeddings $\left( c_{x},\ c_{y} \right)$ where the sentence embeddings is calculated by averaging the embeddings in the final layer. Then the model tries to minimize the following loss function where $y’$ is any sentence in the mini-batch $\mathcal{B}$:
\[\mathcal{L}_{\text{SA}}\left( x,y \right) = - \log\ \frac{e^{c_{x}^{\intercal}c_{y}}}{\sum_{y' \in \mathcal{B}}^{}e^{c_{x}^{\intercal}c_{y'}}}\]
  • Bidirectional Word Alignment:
    This objective encourages bidirectional alignment of word embeddings by leveraging the attention mechanism in the Transformer model by minimizing the distance between the trace of the source-to-target attention $A_{x \rightarrow y}$ and target-to-source attention $A_{y \rightarrow x}$ matrices. Since the Transformer has multiple attention heads, we average the trace of the bidirectional attention matrices generated by all the heads as shown in the following formula:
\[\mathcal{L}_{\text{WA}}\left( x,y \right) = 1 - \frac{1}{H}\sum_{h = 1}^{H}\frac{\text{tr}\left( \left( A_{x \rightarrow y}^{h} \right)^{\intercal}\left( A_{y \rightarrow x}^{h} \right) \right)}{\min\left( \left| x \right|,\left| y \right| \right)}\]

They combined all three objectives to obtain the following total loss function:

\[\mathcal{L}\left( x,y \right) = \mathcal{L}_{\text{MLM}}\left( x,y \right) + \mathcal{L}_{\text{SA}}\left( x,y \right) + \mathcal{L}_{\text{WA}}\left( x,y \right)\]

Notes:

Experiments

AMBER was pre-trained using MLM on the Wikipedia data for 1M steps first using the default hyper-parameters as mBERT found here except that they used a larger batch of 8,192 sentence pairs. Then, they pre-training it using the other two objectives for another 1M steps with a batch of 2,048 sentence pairs from Wikipedia corpus and parallel corpus used to train XLM. As show in the following table, shows the size of AMBER compared to other cross-lingual models:

After pre-training, they fine-tuned AMBER on English annotations and applied the model to predict on non-English data on the following tasks:

  • POS: Cross-lingual Part-Of-Speech (POS) benchmark which contains data in 13 languages. The following table shows that AMBER achieves similar results to XLM-R despite being half its size:
  • PAWS-X: is a paraphrase detection dataset in five different languages. The following table shows that AMBER achieving on-par results with XLM-R large despite the fact that AMBER is one-fifth of its size:
  • XNLI: is a natural language inference dataset in 15 languages. The following table shows that AMBER is not as good as XLM-R on this dataset, but achieving better results than XLM-100.
  • Tatoeba: is a sentence retrieval dataset in 14 different languages; where they try to find the English translation for a given a non-English sentence using maximum cosine similarity. The following table shows AMBER achieving the best results on this benchmark:

The following table summarizes the average results over all languages for the past four benchmarks:

Note:
In all of the previous dataset, AMBER (MLM) is achieving better results than mBERT despite having the same architecture because AMBER uses bigger batch sizes which is proven to be efficient as explain in RoBERTa paper.