Alane Suhr


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Analysis of Language Change in Collaborative Instruction Following
Anna Effenberger | Eva Yan | Rhia Singh | Alane Suhr | Yoav Artzi
Proceedings of the Society for Computation in Linguistics 2022


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Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection
Alane Suhr | Clara Vania | Nikita Nangia | Maarten Sap | Mark Yatskar | Samuel R. Bowman | Yoav Artzi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Crowdsourcing from non-experts is one of the most common approaches to collecting data and annotations in NLP. Even though it is such a fundamental tool in NLP, crowdsourcing use is largely guided by common practices and the personal experience of researchers. Developing a theory of crowdsourcing use for practical language problems remains an open challenge. However, there are various principles and practices that have proven effective in generating high quality and diverse data. This tutorial exposes NLP researchers to such data collection crowdsourcing methods and principles through a detailed discussion of a diverse set of case studies. The selection of case studies focuses on challenging settings where crowdworkers are asked to write original text or otherwise perform relatively unconstrained work. Through these case studies, we discuss in detail processes that were carefully designed to achieve data with specific properties, for example to require logical inference, grounded reasoning or conversational understanding. Each case study focuses on data collection crowdsourcing protocol details that often receive limited attention in research presentations, for example in conferences, but are critical for research success.

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Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior
Noriyuki Kojima | Alane Suhr | Yoav Artzi
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We study continual learning for natural language instruction generation, by observing human users’ instruction execution. We focus on a collaborative scenario, where the system both acts and delegates tasks to human users using natural language. We compare user execution of generated instructions to the original system intent as an indication to the system’s success communicating its intent. We show how to use this signal to improve the system’s ability to generate instructions via contextual bandit learning. In interaction with real users, our system demonstrates dramatic improvements in its ability to generate language over time.

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Analysis of Language Change in Collaborative Instruction Following
Anna Effenberger | Rhia Singh | Eva Yan | Alane Suhr | Yoav Artzi
Findings of the Association for Computational Linguistics: EMNLP 2021

We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.

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Proceedings of the Fourth Workshop on Visually Grounded Interaction and Language
Cătălina Cangea | Abhishek Das | Drew Hudson | Jacob Krantz | Stefan Lee | Jiayuan Mao | Florian Strub | Alane Suhr | Erik Wijmans
Proceedings of the Fourth Workshop on Visually Grounded Interaction and Language


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Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
Alane Suhr | Ming-Wei Chang | Peter Shaw | Kenton Lee
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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. Recently, several datasets, including Spider, were proposed to support development of XSP systems. We propose a challenging evaluation setup for cross-database semantic parsing, focusing on variation across database schemas and in-domain language use. We re-purpose eight semantic parsing datasets that have been well-studied in the setting where in-domain training data is available, and instead use them as additional evaluation data for XSP systems instead. We build a system that performs well on Spider, and find that it struggles to generalize to our re-purposed set. Our setup uncovers several generalization challenges for cross-database semantic parsing, demonstrating the need to use and develop diverse training and evaluation datasets.

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Proceedings of the First Workshop on Interactive and Executable Semantic Parsing
Ben Bogin | Srinivasan Iyer | Victoria Lin | Dragomir Radev | Alane Suhr | Panupong | Caiming Xiong | Pengcheng Yin | Tao Yu | Rui Zhang | Victor Zhong
Proceedings of the First Workshop on Interactive and Executable Semantic Parsing


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Executing Instructions in Situated Collaborative Interactions
Alane Suhr | Claudia Yan | Jack Schluger | Stanley Yu | Hadi Khader | Marwa Mouallem | Iris Zhang | Yoav Artzi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We study a collaborative scenario where a user not only instructs a system to complete tasks, but also acts alongside it. This allows the user to adapt to the system abilities by changing their language or deciding to simply accomplish some tasks themselves, and requires the system to effectively recover from errors as the user strategically assigns it new goals. We build a game environment to study this scenario, and learn to map user instructions to system actions. We introduce a learning approach focused on recovery from cascading errors between instructions, and modeling methods to explicitly reason about instructions with multiple goals. We evaluate with a new evaluation protocol using recorded interactions and online games with human users, and observe how users adapt to the system abilities.

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A Corpus for Reasoning about Natural Language Grounded in Photographs
Alane Suhr | Stephanie Zhou | Ally Zhang | Iris Zhang | Huajun Bai | Yoav Artzi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.


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Learning to Map Context-Dependent Sentences to Executable Formal Queries
Alane Suhr | Srinivasan Iyer | Yoav Artzi
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We propose a context-dependent model to map utterances within an interaction to executable formal queries. To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation. Our approach combines implicit and explicit modeling of references between utterances. We evaluate our model on the ATIS flight planning interactions, and demonstrate the benefits of modeling context and explicit references.

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Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation
Alane Suhr | Yoav Artzi
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a learning approach for mapping context-dependent sequential instructions to actions. We address the problem of discourse and state dependencies with an attention-based model that considers both the history of the interaction and the state of the world. To train from start and goal states without access to demonstrations, we propose SESTRA, a learning algorithm that takes advantage of single-step reward observations and immediate expected reward maximization. We evaluate on the SCONE domains, and show absolute accuracy improvements of 9.8%-25.3% across the domains over approaches that use high-level logical representations.

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Neural Semantic Parsing
Matt Gardner | Pradeep Dasigi | Srinivasan Iyer | Alane Suhr | Luke Zettlemoyer
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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. In the last two years, the models used for semantic parsing have changed dramatically with the introduction of neural encoder-decoder methods that allow us to rethink many of the previous assumptions underlying semantic parsing. We aim to inform those already interested in semantic parsing research of these new developments in the field, as well as introduce the topic as an exciting research area to those who are unfamiliar with it. Current approaches for neural semantic parsing share several similarities with neural machine translation, but the key difference between the two fields is that semantic parsing translates natural language into a formal language, while machine translation translates it into a different natural language. The formal language used in semantic parsing allows for constrained decoding, where the model is constrained to only produce outputs that are valid formal statements. We will describe the various approaches researchers have taken to do this. We will also discuss the choice of formal languages used by semantic parsers, and describe why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. We will then describe a particularly challenging setting for semantic parsing, where there is additional context or interaction that the parser must take into account when translating natural language to formal language, and give an overview of recent work in this direction. Finally, we will introduce some tools available in AllenNLP for doing semantic parsing research.


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A Corpus of Natural Language for Visual Reasoning
Alane Suhr | Mike Lewis | James Yeh | Yoav Artzi
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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. We describe a method of crowdsourcing linguistically-diverse data, and present an analysis of our data. The data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. We experiment with various models, and show the data presents a strong challenge for future research.