787 citations · 2.3k across the 19 of their papers we have counts for
19 papers
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Alex Mallen, Akari Asai, Victor Zhong +3
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying sol…
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…
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…
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…
Improving Intrinsic Exploration with Language Abstractions
Jesse Mu, Victor Zhong, Roberta Raileanu +4
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their envi…
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi +20
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.…
SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark
Victor Zhong, Austin W. Hanjie, Sida I. Wang +2
Existing work in language grounding typically study single environments. How do we build unified models that apply across multiple environments? We propose the multi-environment Sy…
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…
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…
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…
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer +1
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question int…
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
Victor Zhong, Luke Zettlemoyer
Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact ru…
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
Victor Zhong, Caiming Xiong, Nitish Shirish Keskar +1
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear c…
Efficient and Robust Question Answering from Minimal Context over Documents
Sewon Min, Victor Zhong, Richard Socher +1
Neural models for question answering (QA) over documents have achieved significant performance improvements. Although effective, these models do not scale to large corpora due to t…
Global-Locally Self-Attentive Dialogue State Tracker
Victor Zhong, Caiming Xiong, Richard Socher
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the G…
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
Caiming Xiong, Victor Zhong, Richard Socher
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometim…
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Victor Zhong, Caiming Xiong, Richard Socher
A significant amount of the world's knowledge is stored in relational databases. However, the ability for users to retrieve facts from a database is limited due to a lack of unders…
Dynamic Coattention Networks For Question Answering
Caiming Xiong, Victor Zhong, Richard Socher
Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to inc…
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska +6
Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a neural network arc…