Phanxuan Phuc

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I’m a AI Engineer in Vin Bigdata. And I summarize research papers in these topics:

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Published by: Microsoft

∆LM: Delta Language Modeling

DeltaLM (∆LM) is a pre-trained multilingual encoder-decoder model whose encoder and decoder are initialized with a pre-trained multilingual encoder, and trained in a self-supervised way. DeltaLM was proposed by Microsoft in 2021 and published in their paper: “∆LM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders”. The official code for this paper can be found on Microsoft’s GitHub repository: microsoft/deltalm.

In the paper, they decided to use InfoXLM as the pre-trained Multilingual encoder to take advantage of its strong performance. InfoXLM uses the large-scale monolingual data and bilingual data and is jointly trained with a combination of the masked language model (MLM), translation language model (TLM), and cross-lingual contrast (XLCO) objectives. It has a shared vocabulary of 250,000 tokens based on the SentencePiece model.

One challenge is how to initialize the decoder since the architecture of the decoder is different from that of the encoder. To overcome this problem, they introduced a new architecture for the decoder and called it “interleaved decoder”. This new architect has a more consistent structure with the encoder. In this way, the decoder can fully leverage all weights of the pre-trained encoder.

Interleaved Decoder

As shown at the middle of the following figure, the standard Transformer decoder consists of three modules: self-attention, cross-attention, and feed-forward network (FFN). In XLM model, they initialized the self-attention and the FFN with the weights of the pre-trained encoder, while the cross-attention is initialized with either random weights or the same weights as the self-attention. However, to better leverage the full weights of the pre-trained encoder, they proposed a new-yet-similar architecture as seen on the right side of the following figure:

In the interleaved decoder, they interleaved the FFNs and the attention modules, so that the structure is consistent with the pre-trained encoder at the first part and similar to the decoder at the second part. The residual connections and the layer normalizations were performed in each sub-layers in the same way as vanilla Transformer layers.

Now, with the interleaved structure, the decoder can be directly initialized with the pre-trained encoder. More specifically, the self-attentions and the bottom FFNs can be initialized using the odd layers of the InfoXLM pre-trained model, while the cross-attentions and the top FFNs can be initialized with the corresponding even layers.

Pre-training

∆LM was pre-trained using 6TB multilingual data, which is a combination of CC100, CC-Net, and Wikipedia, covering 100 languages. Also, it was pre-trained using 88GB of bilingual data from CCAligned and OPUS, which has 77 languages. DeltaLM was pre-trained on two pre-training tasks:

  • Span Corruption:
    As shown in the following figure, span corruption is the task of reconstructing the text spans based on the masked input sentence. It is proven to be effective for pre-training an encoder-decoder model. In this work, they followed mT5 model to apply this pre-training task to pre-train ∆LM on large-scale monolingual corpora.
  • Translation Span Corruption:
    As shown in the following figure, two parallel sentences are concatenated together, then masked. And the model will have to figure out the text spans based on the input masked translation pair. In this work, they followed mT6 model to leverage large-scale bilingual corpora

DeltaLM was pre-trained the model for $600,000$ steps with $2,048$ samples per batch and the input length was $512$ tokens. For the span corruption task, the probability of corrupted tokens is $15\%$ and the average length of spans is $3$. For the translation span corruption, the probability of corrupted tokens is $50\%$ and the span length is $3$.

Results

In this paper, they were considering the base-size Transformer model, with $768$ hidden size, $3,072$ FFN dimension, $12$ attention heads, and 12 encoder/decoder layers. As said before, DeltaLM was initialized using InfoXLM-BASE. They used the Adam optimizer with $\beta_{1} = 0.9,\ \beta_{2} = 0.999$ with a linear learning rate scheduler with $10,000$ warm-up steps. A gradient clipping of $1.0$ was used.

  • Machine Translation: They evaluated the models on the large-scale WMT-10 benchmark dataset which is a collection of 32.5 million parallel data in English (En), French (Fr), Czech (Cs), German (De), Finnish (Fi), Latvian (Lv), Estonian (Et), Romanian (Ro), Hindi (Hi), Turkish (Tr) and Gujarati (Gu). The evaluation was done on the test dataset:
  • Question Generation: this task takes an answer and the corresponding passage as the input and generates the related question. They used the Chinese XQG dataset where they split into 135k/5k/3k samples as the training/validation/test sets.
  • Abstractive Text Summarization: this task produces the main points of the input documents with new brief sentences. They used the French XGiga where it’s split ito 500k/5k/5k pairs for training/validation/test, respectively.
  • Cross-lingual Text Summarization: This task aims to generate the summary of the input document in different languages. They used WikiLingua dataset which is a large-scale multilingual dataset with about 770k article-summary pairs.
  • Cross-lingual Data-to-text Generation: This task requires an input of multiple triplets and generates a natural description based on the input data. They used WebNLG dataset which is a bilingual dataset of parallel DBpedia triple sets and short texts. The language directions are English-English and English-Russian. It contains about 17k triple sets and 45k short texts in English as well as 7k triple sets and 19k texts in Russian.
  • Zero-shot Abstractive Summarization: They trained the model on the English-English training set and evaluate it on the French-French and Chinese-Chinese test sets. They used XGiga dataset where the training data consists of 50k text-summary pairs, while both the validation and test sets have 5k samples.