200 citations · 1.3k across the 72 of their papers we have counts for
9 papers · 1 filter
Working Memory Graphs
Ricky Loynd, Roland Fernandez, Asli Celikyilmaz +2
Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences. Inspired by this trend, we study the…
Contrastive Multi-document Question Generation
Woon Sang Cho, Yizhe Zhang, Sudha Rao +5
Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents. Such a model is useful in generating clarifying options. Ho…
Robust Navigation with Language Pretraining and Stochastic Sampling
Xiujun Li, Chunyuan Li, Qiaolin Xia +5
Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unsee…
Discourse Understanding and Factual Consistency in Abstractive Summarization
Saadia Gabriel, Antoine Bosselut, Jeff Da +5
We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. Our work addresses current…
Learning Compressed Sentence Representations for On-Device Text Processing
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman +5
Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations a…
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap +3
We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et…
Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
Andrew Hoang, Antoine Bosselut, Asli Celikyilmaz +1
Large-scale learning of transformer language models has yielded improvements on a variety of natural language understanding tasks. Whether they can be effectively adapted for summa…
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Hao Fu, Chunyuan Li, Xiaodong Liu +3
Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. The VAE objective consists of two terms, (i) rec…
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang +4
Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several…