In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires only audio transcription for training -- no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language model, and is fed with letter scores from the acoustic model -- no phonetic word lexicon is needed. Key ingredients for the acoustic model are Gated Linear Units and high dropout. We show near state-of-the-art results in word error rate on the LibriSpeech corpus with MFSC features, both on the clean and other configurations.

A letter-based ConvNet acoustic model leads to a simple and competitive speech recognition pipeline.

Modeling informal inference in natural language is very challenging. With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI from the data? If not, how can NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we aim to answer these questions by enriching the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models with external knowledge further improve the state of the art on the Stanford Natural Language Inference (SNLI) dataset.
the proposed models with external knowledge further improve the state of the art on the SNLI dataset.

Effectively inferring discriminative and coherent latent topics of short texts is a critical task for many real world applications. Nevertheless, the task has been proven to be a great challenge for traditional topic models due to the data sparsity problem induced by the characteristics of short texts. Moreover, the complex inference algorithm also become a bottleneck for these traditional models to rapidly explore variations. In this paper, we propose a novel model called Neural Variational Sparse Topic Model (NVSTM) based on a sparsity-enhanced topic model named Sparse Topical Coding (STC). In the model, the auxiliary word embeddings are utilized to improve the generation of representations. The Variational Autoencoder (VAE) approach is applied to inference the model efficiently, which makes the model easy to explore extensions for its black-box inference process. Experimental results onWeb Snippets, 20Newsgroups, BBC and Biomedical datasets show the effectiveness and efficiency of the model.

a neural sparsity-enhanced topic model based on VAE