Dynamic natural language generation used to rely on templates and subject-attribute dependencies. Writesof empowers writers and marketing professionals to experience a truly dynamic NLG system, one that reasons decisions with correlating audience data, not programmatic algorithms.
With advanced machine learning capabilities, Writesof is leading a new shift in natural langauge generation whereby communication signals are sent and received in near real time. Machines can develop a personality, in our case brand identity, simply by emulating preexisting messages sent and received by one or more persons.
Natural language generation is actually a simple technology, in a logical sense.
Standard methodologies in natural language generation utilize natural language processing tools and decision algorithms that depend mostly on language standardization and occurrence. Statistical models process small strings of text from internally managed phrase and sentence libraries and select words and phrases based on how frequently the strings of text are used in a relative context. In some advanced use cases, millions of computations occur before a single sentence is finally formed.
NLG systems typically employ a small percentage of a language’s total vocabulary. Precisely defined grammar and composition rules are built into dynamic templates. Subject and topic-level language usage is tuned to define the machine-author’s writing style. Message tone and stylistic features are configured as constraints that filter out uninteresting or unintended language use. The entire NLG system is typically built around the audience and message objective, purposed to transform clean data into context phrases that are inserted into dynamic templates that run through decision algorithms to produce uniquely written narratives.
Even the most advanced NLG software available relies primarily template-based natural language generation operations.
In fact, anything more would be overkill, as the type of language used in describing business reports, finance articles, and sports stories are quite similar, in that they describe measurable events that occur on a timeline. Writing about physical products that need to make an emotional connection with a consumer is an entirely different story. Business, finance, and sports, as for written content goes, tend to use highly standardized language. There are only so many ways a writer can say, “he hit a home run in the 5th inning.” and even the most rudimentary natural language technology can handle all of the variants without oversight from a human writer. That just about sums up the most advanced NLG systems
Time. Data. Resources. Your audience is sitting out there waiting to be informed, compelled, and inspired. Making use of your data and 3d party data to deliver messages that result in action. Leave them alone, and they will be misdirected, opportunities lost to thin air. With recent advances in AI and machine learning, Natural Language Generation is no longer relegated to dynamic templates. Today, NLG can produce narratives that learn from your audience and adapt, on message, to shifts in behaviors and interests. The data part is easy. The human element, however, is the critical limitation. Software that interprets data is only as useful as the writer’s ability to uncover insights and construct narratives that consider the myriad of audience engagement metrics that assign valuation to the overall message, as well as each component of a message. Writesof can easily interpret the KPIs that correlate with every aspect of a narrative, comprehensively, in real-time. Every message delivered to an audience has value. Was there a response? What was the response duration? What was the resulting action by the respondent? How did respondent behavior compare with prior responses? Audience analytics data is like a torrent of water that can’t be contained. Much of this data is not utilized simply because we don’t have the time or resources to interpret it, but with data cognition systems, a type of AI, systems can easily find billions of correlations between the message and the audience. But for a writer to look at billions of correlations on a spreadsheet or dashboard would be an impossibility. This technology gap can only be bridged with AI that not only interprets the data, but also takes on the role of the writer. Otherwise, you would need to sift through mountains of data, one narrative at a time, to formulate an optimal messaging strategy. And if your messaging is time sensitive or market sensitive, you would need to continuously adapt to the audience, analyzing incoming data feeds while simultaneously formulating new messages that meet audience needs. Enter Writesof NLG.
human writers with machinesBy from a set of author text files, Writesof’s Automated Copywriting e build Natural Language Generation systems and Natural Language Processing tools are built for e-commerce. We specialize in training AI author voices, which are virtual machines tuned to write in a particular style, and with a measurable goal in mind, for example a fashion brand voice with a goal of improving margin and conversion metrics. The software is programmed to reward high value edits with more experimentation options and restrict editing when it makes a poor decision. Human users can easily override edits or revert changes. Our technology performs best where baseline product pages exist, since it can easily compare key performance indicators from the human-written copy compared with AC’s copy. e aim–for example, rewarding the machinemp, we tune to write like one or more actual writers, based on a collection of retrieved text documents. by analyzing copyto write copy like a human copywriter, a team of writers, or competitor brand writers. All of the content that we produce is a new work of art, not from canned templates. After four years of development, we are pleased to announce the first natural language generation technology that reliably predicts what professional copywriters would write, with author-approval. Ours is the first natural language generation software that learn how to write copy just like you do. that can automatically personalize messages to user groups and adapt language usage