THESIS
2022
1 online resource (xii, 80 pages) : illustrations (some color)
Abstract
Text simplification deals with the problem of making new sentences easier to read and
understand, while retaining the main idea. There are five major approaches for neural
text simplification, including monolingual translation approach and editing approach. A
prominent issue, however, is that they all fail to utilize the rich syntactic and semantic
structure embedded in the sentence. We queried the literature, and classified simplification
operations according to lexical simplification, syntactic simplification and semantic
simplification. We proposed structural simplification, which involves the latter two. There
are three reasons for that: first, structural simplification is less studied; second, it is harder
to implement; third, it is important in reducing reading comprehension diffi...[
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Text simplification deals with the problem of making new sentences easier to read and
understand, while retaining the main idea. There are five major approaches for neural
text simplification, including monolingual translation approach and editing approach. A
prominent issue, however, is that they all fail to utilize the rich syntactic and semantic
structure embedded in the sentence. We queried the literature, and classified simplification
operations according to lexical simplification, syntactic simplification and semantic
simplification. We proposed structural simplification, which involves the latter two. There
are three reasons for that: first, structural simplification is less studied; second, it is harder
to implement; third, it is important in reducing reading comprehension difficulty. To address
structural simplification, we proposed to utilize tree transformer, which is able to
induce a constituency tree structure from the input sentence. We demonstrate that the
tree transformer is comparable, in terms of SARI score, to three strong baselines. In addition,
the tree transformer surpasses transformer baseline in terms of overall correctness,
and is more superior in terms of performing more structural simplification operations,
including syntactic rewrites, semantic rewrites, and sub-sentence deletions.
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