activity
20172022
most citedSeq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

787 citations · 944 across the 8 of their papers we have counts for

collaborators

13 papers

cs.CL20222 cited

RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering

Victor Zhong, Weijia Shi, Wen-tau Yih +1

We introduce RoMQA, the first benchmark for robust, multi-evidence, multi-answer question answering (QA). RoMQA contains clusters of questions that are derived from related constra…

cs.CL2022

M2D2: A Massively Multi-domain Language Modeling Dataset

Machel Reid, Victor Zhong, Suchin Gururangan +1

We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). M2D2 consists of 8.5B tokens and spans 145 domains extracted…

cs.LG20225 cited

Improving Policy Learning via Language Dynamics Distillation

Victor Zhong, Jesse Mu, Luke Zettlemoyer +2

Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how t…

cs.CL20212 cited

LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer

Machel Reid, Victor Zhong

Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coar…

cs.CL2020

Grounded Adaptation for Zero-shot Executable Semantic Parsing

Victor Zhong, Mike Lewis, Sida I. Wang +1

We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines…

cs.CL2019

RTFM: Generalising to Novel Environment Dynamics via Reading

Victor Zhong, Tim Rocktäschel, Edward Grefenstette

Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy l…