We apply various seq2seq models on programming language correction tasks Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets achieve 75% (for C/C++) 56% Java) repair rates these tasks. introduce pyramid encoder in models, which significantly increases the computational efficiency memory efficiency, while achieving similar rate to their nonpyramid counterparts. successf...

Semantic parsing has emerged as a significant and powerful paradigm for natural language interface and question answering systems. Traditional methods of building a semantic parser rely on high-quality lexicons, hand-crafted grammars and linguistic features which are limited by applied domain or representation. In this paper, we propose a general approach to learn from denotations based on Seq2...

In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge performances in many N...

With the continuous growth of online news articles, there arises necessity for an efficient abstractive summarization technique problem information overloading. Abstractive is highly complex and requires a deeper understanding proper reasoning to come up with its own summary outline. task framed as seq2seq modeling. Existing methods perform better on short sequences; however, long sequences, pe...

Abstract Electrical load forecasting is of vital importance in intelligent power management and has been a hot spot industrial Internet application field. Due to the complex patterns dynamics data, accurate short-term still challenging task. Currently, many tasks use deep neural networks for forecasting, most recurrent network as basic architecture, including Long Short-Term Memory (LSTM), Sequ...

Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main writing modes. Most previous sequence-to-sequence (Seq2Seq) models use a single decoder and neglect this fact. In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder...