126 citations · 457 across the 23 of their papers we have counts for
6 papers · 1 filter
Thieves on Sesame Street! Model Extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh +2
We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that…
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
Ran Tian, Shashi Narayan, Thibault Sellam +1
We address the issue of hallucination in data-to-text generation, i.e., reducing the generation of text that is unsupported by the source. We conjecture that hallucination can be c…
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski +3
Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query. In thi…
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh +3
Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the c…
Text Generation with Exemplar-based Adaptive Decoding
Hao Peng, Ankur P. Parikh, Manaal Faruqui +2
We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e.,…
Consistency by Agreement in Zero-shot Neural Machine Translation
Maruan Al-Shedivat, Ankur P. Parikh
Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus…