ラベル Machine Translation の投稿を表示しています。 すべての投稿を表示
ラベル Machine Translation の投稿を表示しています。 すべての投稿を表示

2018年10月18日木曜日

機械翻訳導入に失敗するLSP

世間では、MTだ!PEだ!と言われている(僕の周りだけだと思うが)。MTにしろ、PEMTにしろ、導入に失敗するLSPに共通する点は、これだ!


  1. 社内にポストエディタがいない。
  2. 経営者がMTに反対している。
  3. 社内的にも反MT派が多数を占める。
  4. クライアントからMTを導入して安くしてくれと言われたからイヤイヤ導入している。
  5. なんとなくMTを導入した(導入した特別の理由がない)。
  6. MTの会社に売り込まれて、検証もせず導入した。
  7. 社内でMTPEを検証していない(他社の検証レポートを読んだだけ)。
  8. 社内でMTPEのフローを決めることができない。
  9. CATを使用していない。

我々は、MTPEに将来性を感じました。入念に社内で導入を検証しました。様々な勉強会、セミナー、ワークショップに参加しました。多数のレポートを読みました。CAT開発会社の担当者とも話し合いました。レポートも記事も執筆しました。

我々は、社運をかけて導入を決定しました。検証の段階は完了しています。実ジョブを行っています。半歩ぐらいはリードしていると思っています。

2018年5月27日日曜日

Amazon also starting NMT!

Finally, Amazon is starting neural machine translation service as well. Unfortunately, Japanese is not included yet.

www.機械翻訳.com

Above is translation of an article "AmazonもNMTを開始!" dated November 30, 2017
Translation by Hiroko Matsuda

2018年5月25日金曜日

Judgement Day

There is a topic that has been talked about for a while. That is, a topic concerning humans losing their jobs by the emergence of MT. This is truly a Judgement day. Emergence of MT will cause the human translators to die off. We thought (around) December 31, 2017 would be the Judgement Day...

Today is that day. What happened to the translation industry? Did it collapse?

It did not. Translation companies survived. Translators are doing well. Nothing actually happened.

Manufacturers of car navigation system were having extremely hard time. Sales in car navigation system deteriorated. This is because smartphones replaced the car navigation system. On top of that, the accuracy of the smartphone car navigation system is extremely high. Did all manufacturers for car navigation system go bankrupt? No, they are here to stay. They are not busy having succeeded in the development of the next product that replaces car navigation. The product is an in-vehicle camera. Perhaps you are more familiar with the term drive recorder. The manufacturers for car navigation system won't be destroyed so easily.

The same goes for translation companies. We are not destroyed just by MT. The Judgement day is not here. However, it may not have come and may be just delayed. I will be making my next move so that the day will never come.

Of course, I already have cards in my hand.

www.機械翻訳.com


Above is translation of an article "Judgement Day (審判の日)" dated December 31, 2017
Translation by Hiroko Matsuda

2018年5月23日水曜日

MTPEが過半数を超えました!

MTPE(MT案件)の全案件数に対する比率です、来月は、とうとうMTPE案件が過半数を超えました!本日現在、70%ぐらいです。

来月だけの現象かもしれませんが、とりあえずいただいた案件は丁寧にやっていくしなないと思っています。

www.機械翻訳.com

2018年5月22日火曜日

[Harsh truth] Introduction of MTPE doesn't make translation speed any faster and it won't be any cheaper.

This is truly an unfortunate situation to write about at the end of 2017.

Introduction of MTPE does not make translation speed any faster or any cheaper. I will say this again.

Introduction of MTPE does not make translation speed any faster. Therefore, the price won't go down. 

This is the conclusion we came to after spending a year in 2017 for verification. We tried to use one project and use NMT to some how make the translation speed faster and the price cheaper. In the end, we decided to step down from MTPE.

Most likely, people in the same business as us (translation companies specializing in patent translation) have already noticed. When NMT was made available, the whole industry became excited. Each company came up with services using NMT in various ways. However, it is clear from the fact that MTPE is not offered as a product in the patent translation industry (perhaps this is good news for translators. This means that translators won't lose their jobs by NMT).

NMT cannot translate text using complex sentence structures seen in patent specifications. At first glance, you might think it is translating, but it doesn't come close to a level of "translating" for us. This is because correction is needed across the entire text.

Of course, it may be different between engine to engine, but translation output from NMT is TOEIC 800 level at most. Even if a translator at this level translates (at least regarding patent specification), we can only expect anything but a proper translation. I would be terrified. I would consider rewriting the whole text.

The biggest reason for improper translation is that translation result cannot be controlled. Even a new translator who can only get TOEIC score of 800 can unify terms when they are instructed face to face to unify "determine" being translated as 「判別する」. NMT cannot do this (Adaptive Machine Translation (AMT) can).

Therefore, there is not much of a difference in speed whether humans translate from scratch or using NMT to perform post editing. When MTPE is utilized, upon verifying within the company, it was confirmed that productivity improved to a degree of 20%. However, if we set the target of productivity improvement to 20% or so, combining the existing voice input software, software such as translation memory, and autosuccession function (?) may be enough to achieve this. There's no reason to even bother introducing NMT.

From 2018, we are considering canceling MTPE service for now. If the situation changes, we might reconsider then.

This past year, we made various efforts to incorporate NMT as one of the services. As a conclusion, we chose to step down from it. Starting from 2018, we will be switching our gears to introduce a new service.

*The above article is written only about translating patent specification. When general text is being translated, NMT is effective to an extent.
*Under a condition in which the original specification text is written with post edit in mind, using NMT might be meaningful. Therefore, it would be more meaningful if the text is written under the environment in which the applicant writes the specification and translate within a patent office.


Above is translation of an article "【残酷な事実】MTPEを導入しても翻訳速度は早くはならないし、値段も下がらない。" dated December 30, 2017
Translation by Hiroko Matsuda

2018年5月19日土曜日

What is Adaptive Machine Translation (AMT)?

I'll write about the difference between Neural Machine Translation (NMT) and Adaptive Machine Translation (AMT).

In NMT, when machine translation is executed on a certain text, you cannot control the term translation. Something like this:

English (source)
.......determine............................................................................................... .....................................determines................................................................................determination................................................................................. .....................................................................determined.....................

Japanese (target)
.......判別する............................................................................................... .....................................判断する..................................................................判定する................................................................................. .....................................................................判別する....................

In this manner, there is no uniformity among target translation terms (unable to enforce uniformity).

In AMT:
English (source)
.......determine............................................................................................... .....................................determines................................................................................determination................................................................................. .....................................................................determined.....................

Japanese (target)
.......判別する............................................................................................... .....................................判別する..................................................................判別する................................................................................. .....................................................................判別する....................


When the first "determine" is translated and confirmed with 「判別する」,
the machine translation learns that term and will display the term as「判別する」in the subsequent segments.

In terms of work flow, machine translation is not executed on the entire text in the AMT. The machine translation is executed segment by segment.

Therefore, although the term "post edit" is used on NMT, when AMT is being used, it is an actual "translation" (there is no concept of post editing on the latter).

*We are currently in the process of verifying AMT. If various problems can be solved, we may implement AMT in the first half of 2018.
*Since the name includes "adaptive", I think 「適応性機械翻訳」is correct. However, considering its function and contents,  the name「学習型機械翻訳」(Learning type machine translation) is not wrong.



Above is translation of an article "Adaptive Machine Translation (AMT: 学習型機械翻訳)とは?" dated December 17, 2017
Translation by Hiroko Matsuda

2018年4月24日火曜日

MTPEにも種類があります。

【ハードMTPE】

この方法では、MTをおまけ程度にしか考えません。翻訳の仕上がりは、ポストエディタの力量に大いに左右されます。MT臭さは一切しません。

【ソフトMTPE】

この方法では、MTを最大限に利用します。ポストエディタが付属的なものになります。翻訳の仕上がりは、MTの精度が全てです。ちょっとMT臭さが残ります。

料金にも差異が出てくると思います。あと処理時間も異なるでしょう。

2018年4月20日金曜日

Adaptive neural machine translation

It hereeeeeee! (゚∀゚)
The adaptive neural machine translation (ANMT) is here!!

Is this neural machine translation with artificial intelligence? I'll be investigating closely (if I can, I want to try it out as soon as possible).

Above is translation of an article "Adaptive neural machine translation" dated November 2, 2017
Translation by Hiroko Matsuda

2018年4月18日水曜日

Efficiency of sales strategy

This is something I thought of while I was watching the ENEOS Asia Professional Baseball Championship 2017 (I'm just happy that Japan won). In baseball, the batter might make a sacrifice hit when there is a runner with no out on the first base. After this, one run is scored even with a single hit. Then, it's a change after two withdrawals.

Recently, in high school baseball, there are fewer cases where the game pushes through without making the sacrifice hits when there is a runner with no out on the first base. This is because statistic data shows that the percentage of scoring a run does not change even if the game pulls through without the sacrifice hits.

Let's put this in terms of sales strategy. When comparing the percentage of a sales representative gaining figures by visiting door to door (push-type sales) and the percentage of making figures by advertising online and receiving orders from clients (pull-type sales), I think we can't always be certain that the former always has a higher percentage.

Perhaps using sacrifice hits as an example of sales strategy is inappropriate and meaningless, but I'm just wondering if there is any difference. If not, the latter would be easier not having to walk around. Plus, it's actually annoying, especially in patent offices and law offices to have clients come in without appointments, so I think visiting offices is not a good idea...

If you have any input on this, I'd love to hear them.

Above is translation of an article "営業効率" dated November 21, 2017
Translation by Hiroko Matsuda

2018年4月11日水曜日

It was 7 out of 10

We tried translating the press release written for the company (original text is Japanese) by using machine translation + post editing.

According to the staff who was in charge of the MT + PE, 

"I believe that the accuracy of the MT was about 7 out 10. I felt that company B had more natural translation than company A. When translating term by term, the suggested synonyms were more appropriate (company B more than company A).

Further, the staff thinks that "the productivity increased about 30 to 35% overall". 

With patent text, the productivity increased by 20 to 25%, so it seems that productivity increases more with business texts.

If we can increase this much translation efficiency in total, this may amount to a daily worth of workload per staff.

Currently, we are using machine translation engine from two companies. We will surely continue the verifying process.



Above is translation of an article "10段階で7でした。" dated October 6, 2017
Translation by Hiroko Matsuda 

Interviewed by a local translation school about machine translation

I talked about my thoughts on machine translation for an hour or so. The demand for post editor training is rising, so I think it would be great to work together with translation schools.

The experienced translators are resentful about machine translation. This is why it is difficult for them to shift to being post editors. However, the current translation school students are open-minded, so I believe they are more likely to be accepting of machine translation.

Above is translation of an article "機械翻訳について地元の翻訳学校からインタビューを受けました。" dated September 27, 2017
Translation by Hiroko Matsuda 

Raising awareness about machine translation

Among clients, there are some, especially manufacturers, who feel resentful about machine translation. They won't even consider it.

Regarding pricing, many seem to be interested in the idea of match-rate discounts. If they ask for additional discounts, we reply with "how about machine translation?" but in most cases, they would respond with just "hmmmm".

We need to raise more awareness about machine translation by making press release, hold seminars, or write articles on magazines.

I'd like to put more effort in actions like that from September.

Above is translation of an article "機械翻訳の効用をもっと知っていただきたい" dated August 27, 2017
Translation by Hiroko Matsuda