Search Results for author: Alane Suhr

Found 16 papers, 6 papers with code

Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection

no code implementations EMNLP (ACL) 2021 Alane Suhr, Clara Vania, Nikita Nangia, Maarten Sap, Mark Yatskar, Samuel R. Bowman, Yoav Artzi

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

no code implementations28 Jan 2023 Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, Roy Fox

Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world, which makes learning complex tasks with sparse rewards difficult.

Decision Making Reinforcement Learning (RL)

Continual Learning for Instruction Following from Realtime Feedback

no code implementations19 Dec 2022 Alane Suhr, Yoav Artzi

We study the problem of continually training an instruction-following agent through feedback provided by users during collaborative interactions.

Continual Learning Instruction Following

Abstract Visual Reasoning with Tangram Shapes

no code implementations29 Nov 2022 Anya Ji, Noriyuki Kojima, Noah Rush, Alane Suhr, Wai Keen Vong, Robert D. Hawkins, Yoav Artzi

We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines.

Visual Reasoning

Analysis of Language Change in Collaborative Instruction Following

1 code implementation Findings (EMNLP) 2021 Anna Effenberger, Eva Yan, Rhia Singh, Alane Suhr, Yoav Artzi

We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise.

Instruction Following

Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior

no code implementations10 Aug 2021 Noriyuki Kojima, Alane Suhr, Yoav Artzi

We study continual learning for natural language instruction generation, by observing human users' instruction execution.

Continual Learning

Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing

no code implementations ACL 2020 Alane Suhr, Ming-Wei Chang, Peter Shaw, Kenton Lee

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.

Semantic Parsing

Executing Instructions in Situated Collaborative Interactions

no code implementations IJCNLP 2019 Alane Suhr, Claudia Yan, Charlotte Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, Yoav Artzi

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

1 code implementation23 Sep 2019 Alane Suhr, Yoav Artzi

We show that the performance of existing models (Li et al., 2019; Tan and Bansal 2019) is relatively robust to this potential bias.

A Corpus for Reasoning About Natural Language Grounded in Photographs

2 code implementations ACL 2019 Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, Yoav Artzi

We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language.

Visual Reasoning

Neural Semantic Parsing

no code implementations ACL 2018 Matt Gardner, Pradeep Dasigi, Srinivasan Iyer, Alane Suhr, Luke Zettlemoyer

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.

Code Generation Instruction Following +4

Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation

1 code implementation ACL 2018 Alane Suhr, Yoav Artzi

We propose a learning approach for mapping context-dependent sequential instructions to actions.

Learning to Map Context-Dependent Sentences to Executable Formal Queries

1 code implementation NAACL 2018 Alane Suhr, Srinivasan Iyer, Yoav Artzi

We propose a context-dependent model to map utterances within an interaction to executable formal queries.

Visual Reasoning with Natural Language

no code implementations2 Oct 2017 Stephanie Zhou, Alane Suhr, Yoav Artzi

To understand language in complex environments, agents must reason about the full range of language inputs and their correspondence to the world.

Visual Reasoning

A Corpus of Natural Language for Visual Reasoning

no code implementations ACL 2017 Alane Suhr, Mike Lewis, James Yeh, Yoav Artzi

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.

Question Answering Visual Question Answering (VQA) +1

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