The Open Problems and Solutions of Natural Language Processing

Human beings can experience complex and nuanced emotions, and the unique ability to communicate them through a language as famously coined “putting feeling into words and words into feelings” is what drove to the evolution of pretty much everything. This evolution has pretty much led to our need to communicate with not just humans but with machines also. And the challenge lies with creating a system that reads and understands a text the way a person does, by forming a representation of the desires, emotions, goals, and everything that human forms to understand a text.

With the developments in AI and ML, NLP has seen by far the largest growth and practical implementation than its other counterparts of data science. We are at a juncture in time when an AI assistant can make a call and book appointments at a restaurant in an absolute human-like tone, slang and flow and the real person on the other side can’t differentiate whether he is talking to a human or a bot, that’s how far Natural Language Processing has come.

Problems with Data in NLP and its Solutions

Natural Language Processing is backed by data and whether the currently available data is enough to create an effective system, well it's a tough question, the nature of this data, structured, unstructured or it being too miscellaneous decides the effectiveness of NLP, even if we decide to add more data for the NLP to tackle the scarcity of it, an effective evaluation process is of utmost importance to assess the overall effectiveness of Natural Language Processing. Let’s see one by one the problems and how it can be cracked:

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Evolution of Cross-lingual datasets for the Lack of data on Low resource languages 

There are 7000 languages in the world but only a handful of them are considered resourceful, for example, all over India people speak 23 languages with more than 700 dialects, there are more than 1000 languages in Africa alone, since the number of people who speak them is less and, they receive far less attention. The data available on these low resource languages too is limited but the abundant available data from high resource language can be used to transfer tasks and objectives to a low-resource language but it comes with a problem fortunately for which we have a solution now, for example

 “I’m feeling blue.”

The noun “blue” in the above sentence is an emotion, but it's a color also. The challenge lies in the ability of Natural Language Understanding to successfully transfer the objective of high-resource language text like this to a low-resource language. The available language models are sample efficient only, but with the development of cross-lingual datasets and cross-lingual language such as Transaction Language Modeling (TLM) which use segment embeddings to represent different sentence in a single sequence of input while replace it by language embeddings to represent different language.

Large and Multiple Documents

The current models are based on recurrent neural networks and can not take up an NLU task with a broad context such as reading whole books without scaling up the system. Also, the current models work well at a document level without supervision at tasks like predicting a new chapter or paragraph but flounder at a multi-document level. But as per Cornell University whitepaper on the CFC Network Multi-document question answering model based on the hierarchies of co-attention and self-attention, that refers to multiple documents for finding answers to a query, the NLP can be equipped for reading and processing books and documents of epic proportions.

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Better Evaluation

We need detailed studies and models to understand why certain models work and why some don't and put forth quantifiable measures based on them. The General Language Understanding Evaluation (GLUE) benchmark score is an example of a multi-task language evaluation method. It proposes 

• A benchmark of nine sentence- or sentence-pair language understanding tasks built on established existing datasets and selected to cover a diverse range of dataset sizes, text genres, and degrees of difficulty,

• A diagnostic dataset designed to evaluate and analyze model performance concerning a wide range of linguistic phenomena found in natural language, and

• A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set.

Open Problems with Natural Language Understanding and Solution

To make sense of a sentence or a text remains the most significant problem of understanding a natural language. To breakdown, a sentence into its subject and predicate, identify the direct and indirect objects in the sentence and their relation to various data objects. The literal interpretation of languages could be loose and challenging for machines to comprehend, let's break them down into factors that make it hard and how to crack it.

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The Ambiguity of Natural Language 

The beauty of the natural language is the curse of Natural Language Processing, unique words with different meaning when put in context creates ambiguity on various levels for a machine to process and interpreting such a text in its true semantic sense makes it a loose end.

Too many Synonyms!

In Natural language, we use words with similar meanings or convey a similar idea but are used in different contexts. The words "tall" and "high" are synonyms, the word "tall" can be used to complement a man's height but "high" can not be. It is an absolute necessity in NLP to include the knowledge of synonyms and the specific context where it should be used to create a human-like dialogue.

Watch the Emotion & Tone

Not everybody gets sarcasm and machines also don't get them naturally, the meaning and tone of a message may vary with a person's attitude, his or her slang, the killing details of natural language are way too many for a machine to process and respond.

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Even though emotion analysis has improved overtime still the true interpretation of a text is open-ended.

Natural language processing is the stream of Machine Learning which has taken the biggest leap in terms of technological advancement and growth. Contextual, pragmatic, world knowledge everything has to come together to deliver meaning to a word, phrase, or sentence and it cannot be understood in isolation. With deep learning, linguistic tools and lexical resources have seen advancements in leaps and bounds that make a machine engane in almost human-line sophisticated conversations and it is not the thing of the future but  happening right now.

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