Syntactically Look-Ahead Attention Network for Sentence Compression

Look-Ahead Attention for Generation in Neural Machine Translation

The attention model has become a standard component in neural machine translation (NMT) and it guides translation process by selectively focusing on parts of the source sentence when predicting each target word. However, we find that the generation of a target word does not only depend on the source sentence, but also rely heavily on the previous generated target words, especially the distant w...

Methods for Sentence Compression

Sentence compression is the task of producing a summary of a single sentence. The compressed sentence should be shorter, contain the important content from the original, and itself be grammatical. The three papers discussed here take different approaches to identifying important content, determining which sentences are grammatical, and jointly optimizing these objectives. One family of approach...

A look back, a look ahead.

The experts look ahead

The future of integrated electronics is the future of electronics itself. The advantages of integration will bring about a proliferation of electronics, pushing this science into many new areas. Integrated circuits will lead to such wonders as home computers—or at least terminals connected to a central computer—automatic controls for automobiles, and personal portable communications equipment. ...

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

How to model a pair of sentences is a critical issue in many natural language processing (NLP) tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence separately, without considering the impact of the other sentence; or (iii) relies fully on man...