ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

Decomposing advanced sentences assists to select material in summarization or extract atomic propositions for dilemma answering.

A the latest paper proposes a new pure language processing process, wherever advanced sentences have to be decomposed into a set of straightforward sentences. For instance, the sentence “Sokuhi was born in Fujianand was ordained at 17” is rewritten as sentences “Sokuhi was born in Fujian” and “Sokuhi was ordained at 17”.

Impression credit score: Pxhere, CC0 Community Domain

As most rewrites involve identical functions, scientists propose a neural product that learns to Acknowledge, Split, Duplicate or Fall components of a sentence graph representing term adjacency and grammatical dependencies. The prompt product achieves equivalent or better general performance than baselines. It selectively integrates the linguistic precision of parsing-dependent methods, the expressiveness of graphs, and the power of neural networks for illustration mastering.

Atomic clauses are basic text units for being familiar with advanced sentences. Determining the atomic sentences within advanced sentences is essential for apps such as summarization, argument mining, discourse investigation, discourse parsing, and dilemma answering. Previous work primarily depends on rule-dependent methods dependent on parsing. We propose a new process to decompose each individual advanced sentence into straightforward sentences derived from the tensed clauses in the supply, and a novel challenge formulation as a graph edit process. Our neural product learns to Acknowledge, Split, Duplicate or Fall components of a graph that brings together term adjacency and grammatical dependencies. The complete processing pipeline incorporates modules for graph design, graph modifying, and sentence era from the output graph. We introduce DeSSE, a new dataset created to teach and evaluate advanced sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves equivalent general performance as two parsing baselines on MinWiki. On DeSSE, which has a far more even stability of advanced sentence kinds, our product achieves higher precision on the number of atomic sentences than an encoder-decoder baseline. Success include a detailed mistake investigation.

Research paper: Gao, Y., Ting-hao, Huang, and Passonneau, R. J., “ABCD: A Graph Framework to Convert Sophisticated Sentences to a Masking Set of Very simple Sentences”, 2021. Link: muscles/2106.12027

Maria J. Danford

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