Phanxuan Phuc

fakerphan

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

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Published by: Google Research

Hinted Back-Translation

HintedBT is a family of techniques that provides hints through tags on the source side and target side to the Back-translation mechanism to improve the effectiveness of the provided monolingual data. These techniques were proposed by Google Research in 2021 and published in their paper: HintedBT: Augmenting Back-Translation with Quality and.

Back-translation is one such widely used data augmentation technique in which synthetic parallel data is created by translating monolingual data in the target language to the source language using a baseline system. However, in order to get high quality parallel back-translated (BT) data, we either need a high quality target→source translation model or a high quality parallel (bitext) data. Both are limited when it comes to low-resource languages.

To overcome that, existing methods either use all BT data available which leads to low-quality translation models, or use various cleaning techniques to filter out lower quality BT data which reduces the amount of data available for training low-resource languages even more.

HintedBT provides the solution of this problem by providing two different techniques that can be combined or used independently:

  • Quality Tagging

  • Translit Tagging

Quality Tagging

Quality Tagging approach uses all the BT data by utilizing quality information about each instance by using multiple tags on the source sentence to hint of the quality of the BT pair. For each sentence pair, the quality was determined by the following steps:

  • Use LaBSE to compute sentence embeddings of the two sentences. LaBSE is a BERT-based language agnostic cross-lingual model.

  • Compute the cosine similarity between these source and target embeddings.

  • This score is treated as the quality score

Note:
This method can be seen as an extension to the Tagged BT approach.

Now, we know how they calculated the quality of a sentence pair. Next, we need to know how they designed the binning mechanism. By binning mechanism, I mean how they divided all sentence pairs into bins where each bin has a bunch of sentence pairs. In the paper, they designed the binning mechanism by using:

  • Equal Volume Binning: They calculated the quality score of the $N$ sentence-pairs, then they sorted them by their quality score, and finally divided them into $k$ equally sized groups.

  • Four bins: They experimented with different number of bins (see experiments) and found out that four or three bins provide the best performance.

The following figure shows a sentence-pair after applying Quality Tagging; the tags indicate the bin index prepended to the source sentence:

Note:
They also experimented using Equal Width Binning (to divide the quality score range) instead instead of Equal Volume Binning (to divide the data points range) and found out that the former leads to unbalanced data which isn’t preferable.

Translit Tagging

Translit Tagging approach uses all the BT data by adding a tag to the target sentence indicating whether this sentence pair needs either translation or translation + transliteration . Hence the name “Translit”. If the target was fully translated, the method will prepend <Txn> to the target sentence. Otherwise, it will <Both> to the target sentence as seen below:

Note:
Transliteration is when you sound a word of one language using the scripts of other languages. We do that all the time when writing our names in other language; like “أحمد” will be written in English as “Ahmed”. In the following example, we can see the difference between translation and
translation + transliteration:

Transliteration is a big issue especially in low-resource languages. According to the following table found in the paper, more than 60% of the sentence-pairs in three different low-resource languages contain both translation + transliteration:

This huge percentage of translation + transliteration in the datasets puts a pressure on the trained model to identify implicitly which source words should be translated to the target language and which need to be transliterated. In this paper, they did that by using the following steps:

  • For each word in the source sentence, they used FST (Finite State Transducer) transliteration models from this paper to generate 10 English (i.e., the target language) transliterations.

  • If any of these transliterations are present in the corresponding target, they would categorize this pair as <Both>. As you might have guessed, it only needs one word to prepend this tag.

  • If not, they would categorize it as <Txn>.

Note:
They experimented prepending the translit tag to source sentence but it didn’t perform as we are going to discuss later in the experiments.

Experiments

In this paper, all experiments were performed on three low-resource languages: Hindi→English (hi→en), Gujarati→English (gu→en), and Tamil→English (ta→en). The training/dev/test stats of the data used can be seen in the following table:

All three models used in these experiments were standard Transformer architecture with 6 layers on the encoder and the decoder for hi→en and 4 layers on the decoder for both gu→en and ta→en. The dimension of transformer layers, token embeddings and positional embeddings is $1024$, the feed-forward layer dimension is $8192$, and number of attention heads is $16$.

For training, we use the Adafactor optimizer with $\beta_{1} = 0.9$ and $\beta_{1} = 0.98$, and the learning rate is varied with warm-up for 40,000 steps followed by decay as in the original paper for 300k step using a batch size of 3k across all models and tokenize the source and target using WordPiece tokenization.

Baselines

In the paper, they presented five baseline models:

  • bitext: Model trained only on bitext data.

  • bitext + full-BT: Model trained on bitext data and an additional 23M back-translated pairs.

  • bitext + Iterative-BT: Model trained with two iterations of back-translation (in the forward and reverse directions).

  • bitext + tagged-full-BT: Model trained on bitext data using tagged Back-Translation data. A tag is added to the source in every sentence pair to help the model distinguish between natural (bitext) and synthetic (BT) data.

  • bitext + LaBSE topk-BT: Model trained on bitext data and topk best quality BT pairs. Quality is estimated using LaBSE scores, and they grid-searched with at least 6 LaBSE threshold values and choose the one which gives the best BLEU on the dev set. The chosen threshold yields 20M BT sentences for hi→en, 10M for gu→en and 5M for ta→en.

Results

The following table shows the comparison between different variations of HintedBT and baseline models. As we can see, HintedBT provides the best results on the three languages. Also, quality tagging provides similar results to topk-BT and it’s in face more efficient than topk-BT in terms of computational resources: