Monthly Archives: March 2017

Serializing and Deserializing JLIFF

I’ve been having all kinds of fun saving text (json) representations of translation units (pairs of source and target language strings), sending them from one cloud based service to another and then rebuilding the in-memory object representations from the text representation.

I know that any software engineer will be yawning about now because libraries for doing this kind of thing have existed for a long time. However, it’s been fun for me partly because I’m doing it inside the new Azure Function service, and because some of the objects have abstract relationships (interfaces and sub-classes) introducing some subtleties to getting this to work which took a lot of research to implement.

It relates to the work of the OASIS OMOS TC whose evolving schema for what has been dubbed JLIFF can be seen on GitHub.

The two parts of the object graph requiring the special handling are the array containing the Segment‘s and Ignorable‘s (which implement the ISubUnit interface in my implementation), and the array containing the text and inline markup elements of the Source and Target containers (which implement the IElement interface and subclass AbstractElement in my implementation).

When deserializing the components of these arrays each needs a class which derives from Newtonsoft.Json.JsonConverter.

namespace JliffModel
{
    using System;
    using Newtonsoft.Json;
    using Newtonsoft.Json.Linq;

    public class ISubUnitConverter : JsonConverter
    {
        public override bool CanConvert(Type objectType)
        {
            var canConvert = false;

            if (objectType.Name.Equals("ISubUnit")) canConvert = true;

            return canConvert;
        }

        public override object ReadJson(JsonReader reader, Type objectType, object existingValue, JsonSerializer serializer)
        {
            var jobject = JObject.Load(reader);

            object resolvedType = null;

            if (jobject["type"].Value().Equals("segment")) resolvedType = new Segment();

            serializer.Populate(jobject.CreateReader(), resolvedType);

            return resolvedType;
        }

        public override void WriteJson(JsonWriter writer, object value, JsonSerializer serializer)
        {
            writer.WriteValue(value.ToString());
        }
    }
}

Then the classes derived from JsonConverter are passed into the Deserialize method.

    Fragment modelin = JsonConvert.DeserializeObject<Fragment>(output,
        new ISubUnitConverter(),
        new IElementConverter());

Polymath Service Provider

Over the Christmas break I started to reflect on the nature of service provision in the Language Services industry in the light of new technologies coming out of machine learning and artificial intelligence advances and my own predictions of the influences upon the industry and the industry’s response to them.

There are the recent announcements of adaptive and neural network machine translation; pervasive cloud platforms with ubiquitous connectivity and cognitive capabilities; an upsurge in low-cost, high-benefit open source tooling and frameworks; and many mature api’s and standards.

All of these sophisticated opportunities really do mean that as a company providing services you have to be informed, adaptable, and agile; employ clever, enthusiastic people; and derive joy and satisfaction from harnessing disruptive influences to the benefit of yourselves and your customers.

I do have concerns: How do we sustain the level of investment necessary to stay abreast of all these influences and produce novel services and solutions from them in an environment of very small margins and low tolerance to increased or additional costs?

Don’t get me wrong though. Having spent the last 10 years engaging with world-class research centers such as ADAPT, working alongside thought leading academics and institutions such as DFKI and InfAI, participating in European level Innovation Actions and Projects, and generally ensuring that our company has the required awareness, understanding and expertise, I continue to be positive and enthusiastic in my approach to these challenges.

I am satisfied that we are active in all of the spaces that industry analysts see as being currently significant. To whit: ongoing evaluations of adaptive translation environments and NMT, agile platforms powered by distributed services and serverless architectures, Deep Content (semantic enrichment and NLP), and Review Sentinel (machine learning and text classification).

Less I sound complacent, we have much more in the pipeline and my talented and knowledgeable colleagues are excited for the future.

XLIFF Over the Wire

One of the Technical Committees that I participate in is the OASIS XLIFF OMOS TC. This group is currently working on a json serialization of XLIFF. This fits nicely with our platform of distributed services and providing a standardized, structured format that these services can consume. I’m pleased that the committee members are aligned on the POLA and are working towards an API which is consistent and natural.

One of the use cases could be the simple and fast translation editor which I’ve been amusing myself with, shown below in horizontal layout.

translation-editor-horizontal