arXiv subjects

Dafna Shahaf

Publications and source records attributed to Dafna Shahaf.

At least 37 records · Page 2Linked to original sources

arXiv2022

The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck

Machine learning (ML) is revolutionizing the world, affecting almost every field of science and industry. Recent algorithms (in particular, deep networks) are increasingly data-hungry, requiring large datasets for training. Thus, the dominant paradigm in ML today involves constructing large, task-specific datasets. However, obtaining quality datasets of such magnitude proves to be a difficult challenge. A variety of methods have been proposed to address this data bottleneck problem, but they are scattered across different areas, and it is hard for a practitioner to keep up with the latest developments. In this work, we propose a taxonomy of these methods. Our goal is twofold: (1) We wish to raise the community's awareness of the methods that already exist and encourage more efficient use of resources, and (2) we hope that such a taxonomy will contribute to our understanding of the problem, inspiring novel ideas and strategies to replace current annotation-heavy approaches.

cs.LG↗

arXiv2022

50 Ways to Bake a Cookie: Mapping the Landscape of Procedural Texts

The web is full of guidance on a wide variety of tasks, from changing the oil in your car to baking an apple pie. However, as content is created independently, a single task could have thousands of corresponding procedural texts. This makes it difficult for users to view the bigger picture and understand the multiple ways the task could be accomplished. In this work we propose an unsupervised learning approach for summarizing multiple procedural texts into an intuitive graph representation, allowing users to easily explore commonalities and differences. We demonstrate our approach on recipes, a prominent example of procedural texts. User studies show that our representation is intuitive and coherent and that it has the potential to help users with several sensemaking tasks, including adapting recipes for a novice cook and finding creative ways to spice up a dish.

cs.CL↗

arXiv2022

Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game

Humor is an inherently social phenomenon, with humorous utterances shaped by what is socially and culturally accepted. Understanding humor is an important NLP challenge, with many applications to human-computer interactions. In this work we explore humor in the context of Cards Against Humanity -- a party game where players complete fill-in-the-blank statements using cards that can be offensive or politically incorrect. We introduce a novel dataset of 300,000 online games of Cards Against Humanity, including 785K unique jokes, analyze it and provide insights. We trained machine learning models to predict the winning joke per game, achieving performance twice as good (20\%) as random, even without any user information. On the more difficult task of judging novel cards, we see the models' ability to generalize is moderate. Interestingly, we find that our models are primarily focused on punchline card, with the context having little impact. Analyzing feature importance, we observe that short, crude, juvenile punchlines tend to win.

cs.LG↗

arXiv2022

From Users to (Sense)Makers: On the Pivotal Role of Stigmergic Social Annotation in the Quest for Collective Sensemaking

The web has become a dominant epistemic environment, influencing people's beliefs at a global scale. However, online epistemic environments are increasingly polluted, impairing societies' ability to coordinate effectively in the face of global crises. We argue that centralized platforms are a main source of epistemic pollution, and that healthier environments require redesigning how we collectively govern attention. Inspired by decentralization and open source software movements, we propose Open Source Attention, a socio-technical framework for "freeing" human attention from control by platforms, through a decentralized eco-system for creating, storing and querying stigmergic markers; the digital traces of human attention.

cs.SI↗

arXiv2022

Augmenting Scientific Creativity with an Analogical Search Engine

Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific papers continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large corpus that goes beyond surface matches and simple keywords. Here we contribute the first end-to-end system for analogical search on scientific papers and evaluate its effectiveness with scientists' own problems. Using a human-in-the-loop AI system as a probe we find that our system facilitates creative ideation, and that ideation success is mediated by an intermediate level of matching on the problem abstraction (i.e., high versus low). We also demonstrate a fully automated AI search engine that achieves a similar accuracy with the human-in-the-loop system. We conclude with design implications for enabling automated analogical inspiration engines to accelerate scientific innovation.

cs.HC↗

arXiv2022

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.

cs.HC↗

arXiv2021

Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking

While neural language models often perform surprisingly well on natural language understanding (NLU) tasks, their strengths and limitations remain poorly understood. Controlled synthetic tasks are thus an increasingly important resource for diagnosing model behavior. In this work we focus on story understanding, a core competency for NLU systems. However, the main synthetic resource for story understanding, the bAbI benchmark, lacks such a systematic mechanism for controllable task generation. We develop Dyna-bAbI, a dynamic framework providing fine-grained control over task generation in bAbI. We demonstrate our ideas by constructing three new tasks requiring compositional generalization, an important evaluation setting absent from the original benchmark. We tested both special-purpose models developed for bAbI as well as state-of-the-art pre-trained methods, and found that while both approaches solve the original tasks (>99% accuracy), neither approach succeeded in the compositional generalization setting, indicating the limitations of the original training data. We explored ways to augment the original data, and found that though diversifying training data was far more useful than simply increasing dataset size, it was still insufficient for driving robust compositional generalization (with <70% accuracy for complex compositions). Our results underscore the importance of highly controllable task generators for creating robust NLU systems through a virtuous cycle of model and data development.

cs.CL↗

arXiv2021

E-Commerce Dispute Resolution Prediction

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.

cs.CL↗

arXiv2021

Catchphrase: Automatic Detection of Cultural References

A snowclone is a customizable phrasal template that can be realized in multiple, instantly recognized variants. For example, ``* is the new *" (Orange is the new black, 40 is the new 30). Snowclones are extensively used in social media. In this paper, we study snowclones originating from pop-culture quotes; our goal is to automatically detect cultural references in text. We introduce a new, publicly available data set of pop-culture quotes and their corresponding snowclone usages and train models on them. We publish code for Catchphrase, an internet browser plugin to automatically detect and mark references in real-time, and examine its performance via a user study. Aside from assisting people to better comprehend cultural references, we hope that detecting snowclones can complement work on paraphrasing and help to tackle long-standing questions in social science about the dynamics of information propagation.

cs.CL↗

arXiv2021

How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements

Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: "Are cows more likely to lie down the longer they stand?"). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines.

cs.CL↗

arXiv2021

"Alexa, what do you do for fun?" Characterizing playful requests with virtual assistants

Virtual assistants such as Amazon's Alexa, Apple's Siri, Google Home, and Microsoft's Cortana, are becoming ubiquitous in our daily lives and successfully help users in various daily tasks, such as making phone calls or playing music. Yet, they still struggle with playful utterances, which are not meant to be interpreted literally. Examples include jokes or absurd requests or questions such as, "Are you afraid of the dark?", "Who let the dogs out?", or "Order a zillion gummy bears". Today, virtual assistants often return irrelevant answers to such utterances, except for hard-coded ones addressed by canned replies. To address the challenge of automatically detecting playful utterances, we first characterize the different types of playful human-virtual assistant interaction. We introduce a taxonomy of playful requests rooted in theories of humor and refined by analyzing real-world traffic from Alexa. We then focus on one node, personification, where users refer to the virtual assistant as a person ("What do you do for fun?"). Our conjecture is that understanding such utterances will improve user experience with virtual assistants. We conducted a Wizard-of-Oz user study and showed that endowing virtual assistant s with the ability to identify humorous opportunities indeed has the potential to increase user satisfaction. We hope this work will contribute to the understanding of the landscape of the problem and inspire novel ideas and techniques towards the vision of giving virtual assistants a sense of humor.

cs.HC↗

arXiv2020

Language (Re)modelling: Towards Embodied Language Understanding

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.

cs.CL↗

arXiv2020

Ecological Semantics: Programming Environments for Situated Language Understanding

Large-scale natural language understanding (NLU) systems have made impressive progress: they can be applied flexibly across a variety of tasks, and employ minimal structural assumptions. However, extensive empirical research has shown this to be a double-edged sword, coming at the cost of shallow understanding: inferior generalization, grounding and explainability. Grounded language learning approaches offer the promise of deeper understanding by situating learning in richer, more structured training environments, but are limited in scale to relatively narrow, predefined domains. How might we enjoy the best of both worlds: grounded, general NLU? Following extensive contemporary cognitive science, we propose treating environments as "first-class citizens" in semantic representations, worthy of research and development in their own right. Importantly, models should also be partners in the creation and configuration of environments, rather than just actors within them, as in existing approaches. To do so, we argue that models must begin to understand and program in the language of affordances (which define possible actions in a given situation) both for online, situated discourse comprehension, as well as large-scale, offline common-sense knowledge mining. To this end we propose an environment-oriented ecological semantics, outlining theoretical and practical approaches towards implementation. We further provide actual demonstrations building upon interactive fiction programming languages.

cs.AI↗

arXiv2019

Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from material science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.

cs.LG↗

arXiv2017

Analogy Mining for Specific Design Needs

Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute a novel analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches.

cs.CL↗

arXiv2017

Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons

Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for example, if the data is high-dimensional or unintuitive, or the labels are continuous). In this work, we develop a novel model for crowdsourcing that can complement standard practices by exploiting people's intuitions about groups and relations between them. We employ a recent machine learning setting, called Ballpark Learning, that can estimate individual labels given only coarse, aggregated signal over groups of data points. To address the important case of continuous labels, we extend the Ballpark setting (which focused on classification) to regression problems. We formulate the problem as a convex optimization problem and propose fast, simple methods with an innate robustness to outliers. We evaluate our methods on real-world datasets, demonstrating how useful constraints about groups can be harnessed from a crowd of non-experts. Our methods can rival supervised models trained on many true labels, and can obtain considerably better results from the crowd than a standard label-collection process (for a lower price). By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.

stat.ML↗

arXiv2017

A Machine Learning Approach to Routing

Can ideas and techniques from machine learning be leveraged to automatically generate "good" routing configurations? We investigate the power of data-driven routing protocols. Our results suggest that applying ideas and techniques from deep reinforcement learning to this context yields high performance, motivating further research along these lines.

cs.NI↗

arXiv2017

Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.

cs.CL↗