Translating 100 Billion Words Every Day for E-Commerce with Alibaba Machine Translation

About the Speaker

Chen Bo’xing is a Senior Algorithm Expert in Alibaba Group’s Machine Intelligence Laboratory. His research concentrates on machine translation, natural language processing, and machine learning. Before joining Alibaba Group, he was a researcher (2009–2017) at the National Research Council Canada (NRC). Tracing further back, he served as a researcher in Singapore Information and Communication Research Institution after obtaining his Postdoctoral degrees from Université Grenoble Alpes (France) and FBK-IRST (Italy).

Alibaba’s Machine Translation

You may already be familiar with many machine translation tools, such as Google Translate, Bing Microsoft Translator, and even Youdao Translate. However, many of you may not have heard of Alibaba’s machine translation. Before we dive into the technical details of machine translation, let me share an example of using this technology for cross-border e-commerce. Every year during the Double 11 Shopping Festival, Russia’s logistics system crashes due to large spikes in e-commerce trading. That’s because a lot of Russians buy and sell things from China via AliExpress. How can this happen? Few Russians speak Chinese, and few Chinese sellers know Russian. Well, the answer is by using Alibaba’s translation system.

Alibaba Machine Translation Scenarios and Business Partners

What are the e-commerce scenarios to which we can provide value and use translation? First of all, traffic direction, in-site searches, reservations, and purchase, as well as communication between buyers and sellers. People who have worked on machine translation may know that we may use BLEU Score — a machine translation evaluation standard — to evaluate the machine translation quality, just like we may determine the faithfulness and smoothness of human translation. However, with regards to business, there are some more indicators. For example, has machine translation improved a commodity’s conversion rate? In this way, we can know whether machine translation has brought value to commodities or not.

Machine Translation Challenges for E-commerce Scenarios

There are many challenges for machine translation in the e-commerce field, which may be different from the general fields. First, it has a very high requirement on target language text readability or smoothness. If the translation is very poor and can be hardly understood, users may lose the patience to continue reading. They’ll go to other products instead. Nowadays, the neural networks MT has dramatically improved the translation smoothness, and can almost meet the high requirement on target language readability.

Alibaba Machine Translation Strategies

Alibaba Translation has taken several strategies for all these challenges. These strategies cover data, model, feature, and other aspects of machine translation.

Alibaba MT’s Data

First, Alibaba Translation crawls data from the Internet; this is the primary source of general data. Besides, Alibaba Translation purchases data from or exchanges data with some academic institutions or translation agencies. Alibaba Translation also collects data from the crowdsourcing platform. Alibaba Translation has invested a lot of time and money in data collection. As a result, Alibaba has obtained a vast volume of data in the e-commerce field and has built an enormous data reserve in the MT circle. At present, Alibaba Translation has maintained corpora of over 20 languages.

Alibaba MT Models

Rule-based MT (RBMT)

MT has gone through several development stages: the rule-based MT stage (lasted for 2–3 decades), the statistical MT (SMT) stage (the 1990s–2014), and then the neural networks MT (NMT) stage started from 2014. If you attend academic meetings, you will scarcely see articles about RBMT or SMT. Does it mean that these two models have already been made obsolete? Alibaba Translation believes that these models have their advantages in specific scenarios. For example, although RBMT has not been used for many years, it can deliver excellent results and high accuracy when it has been configured with simple rules and used in concert with dictionaries to translate numbers, dates, addresses, and commodity-related information. Therefore, a part of Alibaba’s translation system is rule-based.


Alibaba’s MT systems also include SMT. Although NMT features terrific smoothness, SMT has its advantages in specific scenarios. For example, product titles are strings made up of independent phrases, and there is no order or logic between them. SMT can deliver outstanding results in this case. Also, when users search for a product, they search by entering one or two phrases. The translation result will be perfect if phrase-based SMT is used (most commonly used academic SMT).


Of course, Alibaba Translation has NMT systems and has developed an RNN-based seq-seq model. In 2017, Alibaba Translation launched a new model — Transformer. The NMT model has the advantage of smooth translation, with good order and logic. For example, when you use NMT to translate from English into Chinese, there will be no English sentence structures remaining in the Chinese sentence. It is suitable for use in strings of 20 to 30 words, such as product description, messages (communication between buyers and sellers), and buyers’ review. Alibaba Translation would usually use two systems for comparison. Although Transformer’s performance is usually better, it does not rule out the case that the RNN-based seq-seq model will have equally good performance. Human evaluation ratings and the testing results are the basis of the final selection.

Alibaba MT Innovation

We are the MT Lab under the DAMO Academy. Apart from work on the existing MT systems, we have a lot of researchers and engineers (with Master’s Degree or Ph.D.) dedicated to innovative work concerning machine learning. A brief introduction is as follows.

Neural Inflection Prediction

The first is about neural inflection prediction. Chinese characters do not have singular or plural forms or tense changes. However, all these must get considered for English. English morphology is relatively simple in comparison with many other languages. For example, Russian. For the same Russian noun, when the stem stays unchanged, there can be dozens of different suffixes. Translating from Chinese or English into Russian will not be able to generate the required suffixes, because they do not exist in the source.

Translation Intervention

Another work is about translation intervention, provided that the MT system has translated key information accurately. However, it is challenging to conduct translation intervention in an NMT system as it does not translate word-by-word. Instead, it reads a sentence, comprehends it, and paraphrases it in the target language. In this case, some information will get missed.

Distributed Training with Model Average

As we have mentioned before, the training corpora have reached a level of more a billion sentences. Using a single GPU for MT training is far from efficient. We need multiple machines and GPUs to divide data into multiple blocks. After each GPU finishes the training, it generates a model. Then we average the models and continue the training. In this way, multiple machines and GPUs can effectively increase the training speed.

Inference Optimization

Alibaba Translation’s goal is to finish translating a sentence with 20–30 words within 100 ms, while many open-source platforms may currently need 1–2s. Google Translate uses TensorFlow code, while Alibaba Translation uses both Python code and TensorFlow code. Google Translate conducts computing in CPU, while Alibaba Translate performs computing in GPU. Alibaba Translation’s strategy is simple but efficient. It is true that computing all the code in GPU will reduce GPU’s service efficiency, but it increases the decoding speed.

Memory Optimization

We have done some memory optimization work; these are mainly engineering strategies.

Knowledge Base Enhanced NMT

We have previously mentioned that we can improve machine translation by using a knowledge base. Therefore, we have left an API in our MT system for the knowledge base. Because the knowledge base is still under construction, we are still primarily using glossaries and bilingual dictionaries. This is a project under cooperation with CAS’s automation research team. Further achievements of this project will be integrated into our MT system as shown in the following figure.

Multi-Modal Translation

Alibaba Translation has also done some multi-modal translation. Of course, the current requests are mainly text translation. At CES 2018, Alibaba Translation showcased a demo for voice translation and will launch an Alibaba voice translation system shortly. We are currently working on translation based on pictures, which requires cooperation with other internal teams.


It is true that machine translation is far from perfect, and its quality is not even close to professional human translation. If a technology is not perfect, can it be used? It depends on the scenario. You may not want to use machine translation to translate formal legal documents. First, it can get used in a cross-border e-commerce environment. When people view commodity information, they can tolerate some trivial issues. Machine translation can provide efficient services for cross-border e-commerce sellers, and bring quite some value to Alibaba.



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