Mingxuan Wang · PaperMetrix

$gen$CNN: A Convolutional Architecture for Word Sequence Prediction

2015 · arXiv (Cornell University)

We propose a novel convolutional architecture, named $gen$CNN, for word sequence prediction. Different from previous work on neural network-based language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history …

On the Sentence Embeddings from Pre-trained Language Models

2020 · arXiv (Cornell University)

Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models without fine-tuning have been found to poorly capture semantic meaning of sentences. In …

"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation

2020 · arXiv (Cornell University)

An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to …

Secoco: Self-Correcting Encoding for Neural Machine Translation

2021 · arXiv (Cornell University)

This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy …

Deep Semantic Role Labeling With Self-Attention

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. However, it …

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

2021