24 citations · 69 across the 6 of their papers we have counts for
6 papers · 1 filter
Attention-guided Generative Models for Extractive Question Answering
Peng Xu, Davis Liang, Zhiheng Huang +1
We propose a novel method for applying Transformer models to extractive question answering (QA) tasks. Recently, pretrained generative sequence-to-sequence (seq2seq) models have ac…
Multiplicative Position-aware Transformer Models for Language Understanding
Zhiheng Huang, Davis Liang, Peng Xu +1
Transformer models, which leverage architectural improvements like self-attention, perform remarkably well on Natural Language Processing (NLP) tasks. The self-attention mechanism…
Decoding and Diversity in Machine Translation
Nicholas Roberts, Davis Liang, Graham Neubig +1
Neural Machine Translation (NMT) systems are typically evaluated using automated metrics that assess the agreement between generated translations and ground truth candidates. To im…
Improve Transformer Models with Better Relative Position Embeddings
Zhiheng Huang, Davis Liang, Peng Xu +1
Transformer architectures rely on explicit position encodings in order to preserve a notion of word order. In this paper, we argue that existing work does not fully utilize positio…
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding
Zhiheng Huang, Peng Xu, Davis Liang +2
Bidirectional Encoder Representations from Transformers (BERT) has recently achieved state-of-the-art performance on a broad range of NLP tasks including sentence classification, m…
Masked Language Model Scoring
Julian Salazar, Davis Liang, Toan Q. Nguyen +1
Pretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are co…