3k citations · 3.3k across the 10 of their papers we have counts for
13 papers
Unsupervised Neural Machine Translation with Generative Language Models Only
Jesse Michael Han, Igor Babuschkin, Harrison Edwards +8
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot a…
On Task-Level Dialogue Composition of Generative Transformer Model
Prasanna Parthasarathi, Arvind Neelakantan, Sharan Narang
Task-oriented dialogue systems help users accomplish tasks such as booking a movie ticket and ordering food via conversation. Generative models parameterized by a deep neural netwo…
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typicall…
Trading Off Diversity and Quality in Natural Language Generation
Hugh Zhang, Daniel Duckworth, Daphne Ippolito +1
For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality…
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
Arvind Neelakantan, Semih Yavuz, Sharan Narang +5
Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, an…
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar +7
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementa…