TechGist05

This is the raw, first draft, and slightly longer rambling version of a post already published on SDL.COM . MT is considered one of the most difficult problems in the general AI and machine learning field. In the field of artificial intelligence , the most difficult problems are informally known as AI-complete problems , implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem— that is, making computers as intelligent as people. It is no surprise that humankind has been working on this problem for almost 70 years now, and is still quite some distance from having solved this problem. “To translate accurately, a machine must be able to understand the text. It must be able to follow the author's argument, so it must have some ability to reason. It must have extensive world knowledge so that it knows what is being discussed — it must at least be familiar with all the same commonsense facts that the average...

This is the second post in our posts series on machine translation quality. Again this is a slightly less polished and raw variant of a version published on the SDL site . The first one focused on BLEU scores , which are often improperly used to make decisions on inferred MT quality, where it clearly is not the best metric to draw this inference. The reality of many of these comparisons today is that scores based on publicly available (i.e. not blind) news domain tests are being used by many companies and LSPs to select MT systems which translate IT, customer support, pharma, financial services domain related content. Clearly, this can only result in sub-optimal choices. The use of machine translation (MT) in the translation industry has historically been heavily focused on localization use cases, with the primary intention to improve efficiency, that is, speed up turnaround and reduce unit word cost. Indeed, machine translation post-editing (MTPE) has been instrumental in helping loca...

This is the first in a series of posts discussing various aspects of MT quality from the context of enterprise use and value, where linguistic quality is important, but not the only determinant of suitability in a structured MT technology evaluation process. A cleaner, more polished, and shorter studio version of this post is available here . You can consider this post a first draft, or the live stage performance (stream of consciousness) version. What is BLEU (Bilingual Evaluation Understudy)? As the use of enterprise machine translation expands, it becomes increasingly more important for users and practitioners to understand MT quality issues in a relevant, meaningful, and accurate way. The BLEU score is a string-matching algorithm that provides basic output quality metrics for MT researchers and developers . In this first post, we will review and look more closely at the  BLEU score , which is probably the most widely used MT quality assessment metric in use by MT researchers an...

This is a guest post by Luigi Muzii that focuses mostly on the various questions that surround Language Data, which by most “big data” definitions and volumes is really not what most in the community would consider big data. As the world hurtles into the brave new world that is being created by a growling volume of machine learning and AI applications, the question of getting the data right is often brushed aside. Most think the data is a solved problem or presume that data is easily available. However, those of us who have been working at MT seriously over the last decade understand this is far from a solved problem. Machines learn from data and smart engineers can find ways to leverage the patterns in data in innumerable ways. Properly used it can make knowledge work easier, or more efficient e.g. machine translation, recommendation, and personalization. The value and quality of this pattern learning can only be as good as the data used, and however exciting all this technology seem...