A method and system for detecting slow page load is provided. An example system comprises a page request detector, a time-out module, a time-out monitor, and a lightweight page requestor. The page request detector may be configured to detect a request for a web page. The time-out module may be configured to commence a time-out period in response to a request for a web page. The time-out module cooperates with the time-out monitor that may be configured to determine that rendering of a rich version of the requested web page has not commenced at an expiration of the time-out period. The lightweight page requestor may be configured to cause a lightweight version of the requested page to be provided to the client system when the time-out monitor determines that the rendering of a rich version of the requested web page has not commenced at an expiration of the time-out period.

A method and system for detecting slow page load is provided. An example system comprises a page request detector, a time-out module, a time-out monitor, and a lightweight page requestor. The page request detector may be configured to detect a request for a web page. The time-out module may be configured to commence a time-out period in response to a request for a web page. The time-out module cooperates with the time-out monitor that may be configured to determine that rendering of a rich version of the requested web page has not commenced at an expiration of the time-out period. The lightweight page requestor may be configured to cause a lightweight version of the requested page to be provided to the client system when the time-out monitor determines that the rendering of a rich version of the requested web page has not commenced at an expiration of the time-out period.

Example systems and methods for displaying an edit tracker of form-based entries on a graphical user interface are described herein. Form based data items including a name and a data entry box are displayed in an area of the graphical user interface. As edits are made to the data items (e.g., changed or new data values are added to the data entry boxes), an edit tracker entry for each changed data item is displayed. The edit tracker entries can include the item name of the data item, the item value of the data item, and the changed item value of the data item. As various modifications are made, some of which may be made within nested or other tabs of the graphical user interface, the edit tracker can include the entries, allowing the user to quickly identify changes, quickly navigate to the changes, and/or undo individual changes.

Example systems and methods for displaying an edit tracker of form-based entries on a graphical user interface are described herein. Form based data items including a name and a data entry box are displayed in an area of the graphical user interface. As edits are made to the data items (e.g., changed or new data values are added to the data entry boxes), an edit tracker entry for each changed data item is displayed. The edit tracker entries can include the item name of the data item, the item value of the data item, and the changed item value of the data item. As various modifications are made, some of which may be made within nested or other tabs of the graphical user interface, the edit tracker can include the entries, allowing the user to quickly identify changes, quickly navigate to the changes, and/or undo individual changes.

Distinguishing pattern differences from non-pattern differences. A set of differences is identified. The set comprises a plurality of differences between first and second versions of a document. A pattern is identified. The pattern explains a transformation from a first string in the first version of the document to a second string in the second version of the document. A subset of differences are identified. The subset comprises a plurality of differences, from among the set, which match the pattern. While presenting a user interface that visually highlights differences between the first and second versions of the document, a first visual treatment is applied to a first difference, based on the first difference being included in the subset. A second visual treatment is also applied to a second difference, based on the second difference being excluded from the subset. The second visual treatment is different than the first visual treatment.

Distinguishing pattern differences from non-pattern differences. A set of differences is identified. The set comprises a plurality of differences between first and second versions of a document. A pattern is identified. The pattern explains a transformation from a first string in the first version of the document to a second string in the second version of the document. A subset of differences are identified. The subset comprises a plurality of differences, from among the set, which match the pattern. While presenting a user interface that visually highlights differences between the first and second versions of the document, a first visual treatment is applied to a first difference, based on the first difference being included in the subset. A second visual treatment is also applied to a second difference, based on the second difference being excluded from the subset. The second visual treatment is different than the first visual treatment.

Presented herein are embodiments of an unsupervised cross-lingual sentiment classification model (which may be referred to as multi-view encoder-classifier (MVEC)) that leverages an unsupervised machine translation (UMT) system and a language discriminator. Unlike previous language model (LM)-based fine-tuning approaches that adjust parameters solely based on the classification error on training data, embodiments employ an encoder-decoder framework of an UMT as a regularization component on the shared network parameters. In one or more embodiments, the cross-lingual encoder of embodiments learns a shared representation, which is effective for both reconstructing input sentences of two languages and generating more representative views from the input for classification. Experiments on five language pairs verify that an MVEC embodiment significantly outperforms other models for 8/11 sentiment classification tasks.

Presented herein are embodiments of an unsupervised cross-lingual sentiment classification model (which may be referred to as multi-view encoder-classifier (MVEC)) that leverages an unsupervised machine translation (UMT) system and a language discriminator. Unlike previous language model (LM)-based fine-tuning approaches that adjust parameters solely based on the classification error on training data, embodiments employ an encoder-decoder framework of an UMT as a regularization component on the shared network parameters. In one or more embodiments, the cross-lingual encoder of embodiments learns a shared representation, which is effective for both reconstructing input sentences of two languages and generating more representative views from the input for classification. Experiments on five language pairs verify that an MVEC embodiment significantly outperforms other models for 8/11 sentiment classification tasks.

A method for maintaining modification management of a tailored document based on transcluded portions of one or more source documents, comprising determining modifications in each revision of each source document, storing said modifications, and notifying a user of the tailored document with respect to said determined modifications in each source document that are incorporated in the tailored document, wherein said modifications include any changes in the source documents between revisions of the source document.

A method for maintaining modification management of a tailored document based on transcluded portions of one or more source documents, comprising determining modifications in each revision of each source document, storing said modifications, and notifying a user of the tailored document with respect to said determined modifications in each source document that are incorporated in the tailored document, wherein said modifications include any changes in the source documents between revisions of the source document.