Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder model for sentence level TS, which makes minimal assumptions about word sequence. We conduct preliminary experiments to find that the model is able to learn operation rules such as reversing, sorting and replacing from sequence pairs, which shows that the model may potentially discover and apply rules such as modifying sentence structure, substituting words, and removing words for TS.
Automatic TS is a complicated natural language processing (NLP) task, it consists of lexical and syntactic simplification levels. Usually, hand-crafted, supervised, and unsupervised methods based on resources like English Wikipedia (EW) and Simple English Wikipedia (SEW) @cite_18 are utilized for extracting simplification rules. It is very easy to mix up the automatic TS task and the automatic summarization task @cite_7 @cite_12 . TS is different from text summarization as the focus of text summarization is to reduce the length and redundant content.
@cite_18 @cite_12 @cite_7
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Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification. Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we propose to use the Long Short-Term Memory (LSTM) Encoder-Decoder model for sentence level TS, which makes minimal assumptions about word sequence. We conduct preliminary experiments to find that the model is able to learn operation rules such as reversing, sorting and replacing from sequence pairs, which shows that the model may potentially discover and apply rules such as modifying sentence structure, substituting words, and removing words for TS. Many corpus-based natural language processing systems rely on text corpora that have been manually annotated with syntactic or semantic tags. In particular, all previous dictionary construction systems for information extraction have used an annotated training corpus or some form of annotated input. We have developed a system called AutoSlog-TS that creates dictionaries of extraction patterns using only untagged text. AutoSlog-TS is based on the AutoSlog system, which generated extraction patterns using annotated text and a set of heuristic rules. By adapting AutoSlog and combining it with statistical techniques, we eliminated its dependency on tagged text. In experiments with the MUG-4 terrorism domain, AutoSlog-TS created a dictionary of extraction patterns that performed comparably to a dictionary created by AutoSlog, using only preclassified texts as input. Supervised approaches to NLP tasks rely on high-quality data annotations, which typically result from expensive manual labelling procedures. For some tasks, however, the subjectivity of human judgements might reduce the usefulness of the annotation for real-world applications. In Machine Translation (MT) Quality Estimation (QE), for instance, using humanannotated data to train a binary classifier that discriminates between good (useful for a post-editor) and bad translations is not trivial. Focusing on this binary task, we show that subjective human judgements can be effectively replaced with an automatic annotation procedure. To this aim, we compare binary classifiers trained on different data: the human-annotated dataset from the 7 th Workshop on Statistical Machine Translation (WMT-12), and an automatically labelled version of the same corpus. Our results show that human labels are less suitable for the task.