Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Competency Problems: On Finding and Removing Artifacts in Language Data
Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of…
Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
State-of-the-art neural machine translation models generate outputs autoregressively, where every step conditions on the previously generated tokens. This sequential nature causes inherent decoding…
Random Feature Attention
Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at every timestep.…
Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics
Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their…
S2AND: A Benchmark and Evaluation System for Author Name Disambiguation
Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of digital library…
COVR: A test-bed for Visually Grounded Compositional Generalization with real images
While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to synthetic images. In…
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules
One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users’ commands, a task trivial for humans due to their common sense. In this…
General-Purpose Question-Answering with Macaw
Despite the successes of pretrained language models, there are still few high-quality, general-purpose QA systems that are freely available. In response, we present MACAW, a versatile, generative…
Domain-Specific Multi-Level IR Rewriting for GPU: The Open Earth Comp
Most compilers have a single core intermediate representation (IR) (e.g., LLVM) sometimes complemented with vaguely defined IR-like data structures. This IR is commonly low-level and close to…
Factorizing Perception and Policy for Interactive Instruction Following
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The ‘interactive instruction following’ task attempts to…