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Found 16 papers, 6 papers with code
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection
Even though it is such a fundamental tool in NLP, crowdsourcing use is largely guided by common practices and the personal experience of researchers.
Do Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world, which makes learning complex tasks with sparse rewards difficult.
Continual Learning for Instruction Following from Realtime Feedback
We study the problem of continually training an instruction-following agent through feedback provided by users during collaborative interactions.
Abstract Visual Reasoning with Tangram Shapes
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines.
Analysis of Language Change in Collaborative Instruction Following
We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise.
Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior
We study continual learning for natural language instruction generation, by observing human users' instruction execution.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training.
Executing Instructions in Situated Collaborative Interactions
We study a collaborative scenario where a user not only instructs a system to complete tasks, but also acts alongside it.
NLVR2 Visual Bias Analysis
We show that the performance of existing models (Li et al., 2019; Tan and Bansal 2019) is relatively robust to this potential bias.
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task.
Ranked #10 on
Vision and Language Navigation
on Touchdown Dataset
A Corpus for Reasoning About Natural Language Grounded in Photographs
We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language.
Neural Semantic Parsing
Semantic parsing, the study of translating natural language utterances into machine-executable programs, is a well-established research area and has applications in question answering, instruction following, voice assistants, and code generation.
Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation
We propose a learning approach for mapping context-dependent sequential instructions to actions.
Learning to Map Context-Dependent Sentences to Executable Formal Queries
We propose a context-dependent model to map utterances within an interaction to executable formal queries.
Visual Reasoning with Natural Language
To understand language in complex environments, agents must reason about the full range of language inputs and their correspondence to the world.
A Corpus of Natural Language for Visual Reasoning
We present a new visual reasoning language dataset, containing 92, 244 pairs of examples of natural statements grounded in synthetic images with 3, 962 unique sentences.
