はじめに
対話型AIプラットフォーム
チャットボットの概要
自然言語処理(NLP)
ボットの概念と用語
クイックスタートガイド
プラットフォームへのアクセス
ボットビルダーの操作
リリースノート
最新バージョン(英語)
以前のバージョン(英語)
廃止機能(英語)
コンセプト
設計
ストーリーボード
ダイアログタスク
ダイアログタスクとは
ダイアログビルダー
ノードタイプ
インテントノード
ダイアログノード
エンティティノード
フォームノード
確認ノード
ロジックノード
ボットアクションノード
サービスノード
Webhookノード
スクリプトノード
グループノード
エージェント転送ノード
ユーザープロンプト
音声通話プロパティ
イベント ハンドラー
ナレッジグラフ
ナレッジグラフの抽出
ナレッジグラフの構築
ボットにナレッジグラフを追加
グラフの作成
ナレッジグラフの構築
既存のソースからFAQを構築
特性、同義語、停止用語
変数ネームスペースの管理
更新
ノード間の質問と回答の移動
用語の編集と削除
質問と応答の編集
ナレッジグラフの分析
通知タスク
スモールトーク
デジタルスキル
デジタルフォーム
デジタルビュー
デジタルビューとは
パネル
ウィジェット
トレーニング
トレーニングとは
機械学習
機械学習とは
モデル検証
ファンダメンタルミーニング
ナレッジグラフ
示唆
ランキングおよび解決
NLPの詳細設定
NLPのガイドライン
インテリジェンス
インテリジェンスとは
コンテキスト
コンテキストインテント
割り込み
複数インテントの検出
エンティティの変更
デフォルトの会話
センチメント管理
トーン分析
テストとデバッグ
ボットと会話
発話テスト
バッチテスト
会話テスト
デプロイ
チャネル
公開
分析
ボットの分析
NLPメトリクス
会話フロー
Usage Metrics
封じ込め測定
カスタムダッシュボード
カスタムダッシュボードとは
メタタグ
カスタムダッシュボードとウィジェット
ユニバーサルボット
ユニバーサルボットとは
ユニバーサルボットの定義
ユニバーサルボットの作成
ユニバーサルボットのトレーニング
ユニバーサルボットのカスタマイズ
他言語の有効化
ストア
プラントと使用
Overview
Usage Plans
Support Plans
Invoices
管理
ボット認証
複数言語対応ボット
個人を特定できる情報の編集
ボット変数の使用
IVRのシステム連携
一般設定
ボット管理
ハウツー
会話スキルの設計
バンキングボットを作成
バンキングボット – 資金の振り替え
バンキングボット – 残高を更新
ナレッジグラフを構築
スマートアラートの予約方法
デジタルスキルの設計
デジタルフォームの設定方法
デジタルビューの設定方法
データテーブルのデータの追加方法
データテーブルのデータの更新方法
Add Data from Digital Forms
ボットのトレーニング
示唆の使用方法
インテントとエンティティのパターンの使用方法
コンテキスト切り替えの管理方法
ボットのデプロイ
エージェント転送の設定方法
ボット関数の使用方法
コンテンツ変数の使用方法
グローバル変数の使用方法
Web SDK Tutorial(英語)
Widget SDK Tutorial(英語)
ボットの分析
カスタムダッシュボードの作成方法
カスタムタグを使ってフィルタリング
管理
ボット管理者コンソール
ダッシュボード
ユーザーの管理
ユーザーの管理
グループの管理
ロール管理
ボット管理モジュール
登録
ユーザーの招待
招待状の一括送信
ユーザーデータのインポート
Active Directoryからユーザーを同期
セキュリティ/コンプライアンス
シングル サインオンの使用
セキュリティ設定
Billing(日本未対応)
  1. ホーム
  2. Docs
  3. Virtual Assistants
  4. Advanced Topics
  5. Language Management
  6. Managing Translation Services

Managing Translation Services

The Kore.ai XO Platform offers multiple ways to train your virtual assistant for language understanding. One of the ways is to use translation services to translate the user input. In this approach, you can train the virtual assistant in a language (NLU Language) other than the interaction language. For example, you can enable Spanish as an interaction language but train the assistant using English language training data.

Translation services can also be used for translating the bot responses if they are defined in a language other than the conversation language. The Platform allows you to define language-specific responses for each of the languages enabled for the assistant. However, you may choose to write responses in a language other than the enabled language.

The Platform provides out-of-the-box support for Microsoft Translator and Google Translation APIs. You can also use the Custom Translation Engine feature to integrate with any other translation services or your in-house translation services.

Configuring Microsoft Translator Service

To enable automatic translation using Microsoft Translation Services, please follow the steps below:

  1. Go to Build > Configurations > Languages > Translation Configurations.
  2. Select Microsoft Translator.
  3. Provide the API Key of your Microsoft Translator API service. Learn More.
  4. Click Save to complete the setup.

Configuring Google Translation Service

To enable automatic translation using Google Translation Services, please follow the steps below: 

  1. Go to Build > Configurations > Languages > Translation Configurations.
  2. Select Google Translator.
  3. Provide the API Key of your Google Translation API service. Learn More.
  4. Click Save to complete the setup.

Configuring Custom Translation Service

The Custom Translation Service allows you to use translation services by integrating with other translation providers or to integrate with any in-house translation services you may have.

How it works

Here is how custom translation services work:

  1. Follow the instructions below to enable the Custom Translation Engine feature.
  2. You can set up the integration with your translation service APIs using the Get or Post method.
  3. Refer to your translation service documentation for the authentication mechanism, request payload, and response payload.
  4. This integration is used for translating both the user input as well as the bot responses. The platform will automatically make the following information available in the context during runtime.
  5. It will make use of the following functions while defining the request payload.
    1. koreUtil.conversation.sourceText()– This function will return the text to be translated.
      1. If the user’s input is being translated, then the function will return the user’s input.
      2. If the bot response is being translated, then the function will return the bot response.
    2. For translating user input, this function will return the user input. For translating the bot response, the function will return the bot response text.
    3. koreUtil.conversation.getSourceLanguage() – This function returns the current language of the text to be translated.
      1. If the user input is being translated, then the function will return the language in which the user is interacting.
      2. If the bot response is being translated, then the function will return the language in which the response is written.
    4. koreUtil.conversation.getTargetLanguage() – This function returns the language to which the text should be translated to.
      1. If the user input is being translated, then the function will return the language to which the input should be translated.
      2. If the bot response is being translated, then the function will return the language to which the response should be translated.The platform invokes the translation service using the defined configurations.
  6. The translation engine should share the translated text as part of the API response.
  7. Map the relevant field from the response payload to be used as the translated text.

Enabling a Custom Translation Engine

  1. Go to Build > Configurations > Languages > Translation Configurations.
  2. Select Custom, and choose Add Custom Engine from the dropdown.

  3. Provide a name for the Custom Translation Engine.
  4. Define the request payload for sending the text to be translated. Refer to the details provided above for defining the request payload.
  5. Refer to the Service Node documentation to learn more about how to configure service integrations.

  6. After defining the request payload, you can test the integration by providing the required details from the Test Request tab. Provide the sample values for the variables shown under the Sample Context Values sections, click Test to verify if the custom translation connection is established.

  7. After a successful test, the platform displays the API response received from the translation service.
  8. Verify the response payload and map the translated text from the payload in the Translated Output field.

  9. Click the Extract button to verify if the translation output is correctly mapped.

  10. Click Save & Exit to return to the Languages page.
  11. Click Save to complete the configuration.

Managing Translation Services

The Kore.ai XO Platform offers multiple ways to train your virtual assistant for language understanding. One of the ways is to use translation services to translate the user input. In this approach, you can train the virtual assistant in a language (NLU Language) other than the interaction language. For example, you can enable Spanish as an interaction language but train the assistant using English language training data.

Translation services can also be used for translating the bot responses if they are defined in a language other than the conversation language. The Platform allows you to define language-specific responses for each of the languages enabled for the assistant. However, you may choose to write responses in a language other than the enabled language.

The Platform provides out-of-the-box support for Microsoft Translator and Google Translation APIs. You can also use the Custom Translation Engine feature to integrate with any other translation services or your in-house translation services.

Configuring Microsoft Translator Service

To enable automatic translation using Microsoft Translation Services, please follow the steps below:

  1. Go to Build > Configurations > Languages > Translation Configurations.
  2. Select Microsoft Translator.
  3. Provide the API Key of your Microsoft Translator API service. Learn More.
  4. Click Save to complete the setup.

Configuring Google Translation Service

To enable automatic translation using Google Translation Services, please follow the steps below: 

  1. Go to Build > Configurations > Languages > Translation Configurations.
  2. Select Google Translator.
  3. Provide the API Key of your Google Translation API service. Learn More.
  4. Click Save to complete the setup.

Configuring Custom Translation Service

The Custom Translation Service allows you to use translation services by integrating with other translation providers or to integrate with any in-house translation services you may have.

How it works

Here is how custom translation services work:

  1. Follow the instructions below to enable the Custom Translation Engine feature.
  2. You can set up the integration with your translation service APIs using the Get or Post method.
  3. Refer to your translation service documentation for the authentication mechanism, request payload, and response payload.
  4. This integration is used for translating both the user input as well as the bot responses. The platform will automatically make the following information available in the context during runtime.
  5. It will make use of the following functions while defining the request payload.
    1. koreUtil.conversation.sourceText()– This function will return the text to be translated.
      1. If the user’s input is being translated, then the function will return the user’s input.
      2. If the bot response is being translated, then the function will return the bot response.
    2. For translating user input, this function will return the user input. For translating the bot response, the function will return the bot response text.
    3. koreUtil.conversation.getSourceLanguage() – This function returns the current language of the text to be translated.
      1. If the user input is being translated, then the function will return the language in which the user is interacting.
      2. If the bot response is being translated, then the function will return the language in which the response is written.
    4. koreUtil.conversation.getTargetLanguage() – This function returns the language to which the text should be translated to.
      1. If the user input is being translated, then the function will return the language to which the input should be translated.
      2. If the bot response is being translated, then the function will return the language to which the response should be translated.The platform invokes the translation service using the defined configurations.
  6. The translation engine should share the translated text as part of the API response.
  7. Map the relevant field from the response payload to be used as the translated text.

Enabling a Custom Translation Engine

  1. Go to Build > Configurations > Languages > Translation Configurations.
  2. Select Custom, and choose Add Custom Engine from the dropdown.

  3. Provide a name for the Custom Translation Engine.
  4. Define the request payload for sending the text to be translated. Refer to the details provided above for defining the request payload.
  5. Refer to the Service Node documentation to learn more about how to configure service integrations.

  6. After defining the request payload, you can test the integration by providing the required details from the Test Request tab. Provide the sample values for the variables shown under the Sample Context Values sections, click Test to verify if the custom translation connection is established.

  7. After a successful test, the platform displays the API response received from the translation service.
  8. Verify the response payload and map the translated text from the payload in the Translated Output field.

  9. Click the Extract button to verify if the translation output is correctly mapped.

  10. Click Save & Exit to return to the Languages page.
  11. Click Save to complete the configuration.
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