New Technology, Old Problems: The Missing Voices in Natural Language Processing

Natural Language Processing can be applied into various areas like Machine Translation, Email Spam detection, Information Extraction, Summarization, Question Answering etc. Next, we discuss some of the areas with the relevant work done in those directions. To generate a text, we need to have a speaker or an application and a generator or a program that renders the application’s intentions into a fluent phrase relevant to the situation. For such a low gain in accuracy, losing all explainability seems like a harsh trade-off. However, with more complex models we can leverage black box explainers such as LIME in order to get some insight into how our classifier works. If we are getting a better result while preventing our model from “cheating” then we can truly consider this model an upgrade.

The company employs copywriters who write articles that mention particular keywords. This is in order to fill websites with content so that google would show them higher up in their search ranking. The company decides they can’t afford to pay copywriters and they would like to somehow automate the creation of those SEO-friendly articles.

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The recent NarrativeQA dataset is a good example of a benchmark for this setting. Reasoning with large contexts is closely related to NLU and requires scaling up our current systems dramatically, until they can read entire books and movie scripts. A key question here—that we did not have time to discuss during the session—is whether we need better models or just train on more data. Benefits and impact   Another question enquired—given that there is inherently only small amounts of text available for under-resourced languages—whether the benefits of NLP in such settings will also be limited. Stephan vehemently disagreed, reminding us that as ML and NLP practitioners, we typically tend to view problems in an information theoretic way, e.g. as maximizing the likelihood of our data or improving a benchmark. Taking a step back, the actual reason we work on NLP problems is to build systems that break down barriers.

What we should focus on is to teach skills like machine translation in order to empower people to solve these problems. Academic progress unfortunately doesn’t necessarily relate to low-resource languages. However, if cross-lingual benchmarks become more pervasive, then this should also lead to more progress on low-resource languages. Innate biases vs. learning from scratch   A key question is what biases and structure should we build explicitly into our models to get closer to NLU. Similar ideas were discussed at the Generalization workshop at NAACL 2018, which Ana Marasovic reviewed for The Gradient and I reviewed here. Many responses in our survey mentioned that models should incorporate common sense.

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Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas. Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day. Inclusiveness, however, should not be treated as solely a problem of data acquisition.

• This is where contextual embedding comes into play and is used to learn sequence-level semantics by taking into consideration the sequence of all words in the documents.
• Although NLP models are inputted with many words and definitions, one thing they struggle to differentiate is the context.
• The naïve bayes is preferred because of its performance despite its simplicity (Lewis, 1998) [67] In Text Categorization two types of models have been used (McCallum and Nigam, 1998) [77].
• Hidden Markov Models are extensively used for speech recognition, where the output sequence is matched to the sequence of individual phonemes.

In the case of a domain specific search engine, the automatic identification of important information can increase accuracy and efficiency of a directed search. There is use of hidden Markov models (HMMs) to extract the relevant fields of research papers. These extracted text segments are used to allow searched over specific fields and to provide effective presentation of search results and to match references nlp problems to papers. For example, noticing the pop-up ads on any websites showing the recent items you might have looked on an online store with discounts. In Information Retrieval two types of models have been used (McCallum and Nigam, 1998) [77]. But in first model a document is generated by first choosing a subset of vocabulary and then using the selected words any number of times, at least once without any order.