Recent efforts towards Machine Reading Comprehension : A Literature Review Marı́a

Natural language understanding (NLU) is considered a subfield of natural language processing (NLP) that deals with machine reading comprehension. Since the 60’s there have been efforts towards the advancement in this task. Most of these previous systems share similar components: a lexicon of the language and a parser and grammar rules to break sentences into an internal representation. Building a rich lexicon with an adequate ontology like Wordnet required many person-years [Miller et al.1990]. Regardless of these efforts progress in this area has been slow and NLU is considered an AI-hard problem. Hence, recently, machine learning approaches to reading comprehension have been taken. One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent [Weston et al.2015]. In fact, besides the theoretical interest, there is considerable commercial interest in machine reading comprehension because of its application to gathering, mining and analysis of large scale unstructured data such as news and voice. IBM’s Watson and Apple’s Siri are examples of this trend. Question-answering is another subfield of NLP and Information Retrieval that is related to machine comprehension because it aims to build systems that automatically answer questions posed by humans in natural language. Currently, Machine reading comprehension lies in the intersection of natural language processing (NLP), machine learning (ML) and question-answering (QA). Typically, machine reading comprehension can be defined as the ability of a machine system to read and understand natural language documents at a sufficient level where it is capable of answering questions based on the original text. Unlike tasks like dialogue or summarization, this evaluation approach (i.e. based on questions and answers) is easier to grade and thus makes it an appealing research avenue [Weston et al.2015]. Although there is an evaluation criteria, teaching machines to really understand natural language and be able to correctly answer questions remains a puzzling challenge. The challenge can be divided into two sequential and interrelated sub-problems. First, machine reading comprehension models are hard on its own: it is very difficult to structure and build models flexible enough to learn to exploit document structure. Humans do not understand language in isolation, the context in which sentences and words are understood plays an important role in human comprehension, so the question is if machines can exploit this context -and howto make predictions about natural language [Hill et al.2015]. Second, these models are trained and evaluated using the available data and assessing how good the proposed model answers to questions about a given text. The dilemma is that for this evaluation to be meaningful, adequate training and testing datasets are essential. A clear example of this limitation is that supervised machine learning approaches have largely been absent due to both the lack of large scale training dataset and the difficulty in structuring statistical models flexible enough to learn to exploit document structure [Hermann et al.2015]. Accordingly, high-quality, real and large datasets play an crucial role to make progress on machine comprehension. The complication is that already existent datasets suffer from shortcomings such as being too small but realistic or large but semi-synthetic, thus not realistic. In response to this, recently there has been two major efforts towards the advance of machine reading comprehension: creation of datasets and development of models.