Angela Fan

Publications45
h-index19
Citations4,182
Highly Influential Citations636
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Language Modeling with Gated Convolutional Networks
TLDR
A finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens, is developed and is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks. Expand
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
TLDR
Fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks and supports distributed training across multiple GPUs and machines. Expand
Hierarchical Neural Story Generation
TLDR
This work collects a large dataset of 300K human-written stories paired with writing prompts from an online forum that enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. Expand
Wizard of Wikipedia: Knowledge-Powered Conversational agents
TLDR
The best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while a new benchmark allows for measuring further improvements in this important research direction. Expand
Pay Less Attention with Lightweight and Dynamic Convolutions
TLDR
It is shown that a very lightweight convolution can perform competitively to the best reported self-attention results, and dynamic convolutions are introduced which are simpler and more efficient than self-ATTention. Expand
Reducing Transformer Depth on Demand with Structured Dropout
TLDR
LayerDrop, a form of structured dropout, is explored, which has a regularization effect during training and allows for efficient pruning at inference time, and shows that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. Expand
Controllable Abstractive Summarization
TLDR
A neural summarization model with a simple but effective mechanism to enable users to specify high level attributes in order to control the shape of the final summaries to better suit their needs. Expand
Strategies for Structuring Story Generation
Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts… Expand
ELI5: Long Form Question Answering
TLDR
This work introduces the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions, and shows that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. Expand
KILT: a Benchmark for Knowledge Intensive Language Tasks
TLDR
It is found that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. Expand
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