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data/users/kkeith/seedset-kkeith-maple.json

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+{
+  "username": "kkeith",
+  "s2_authorid": "145137850",
+  "papers": [
+    {
+      "title": "Proximal Causal Inference With Text Data",
+      "abstract": [
+        "Recent text-based causal methods attempt to mitigate confounding bias by including unstructured text data as proxies of confounding variables that are partially or imperfectly measured.",
+        "These approaches assume analysts have supervised labels of the confounders given text for a subset of instances, a constraint that is not always feasible due to data privacy or cost.",
+        "Here, we address settings in which an important confounding variable is completely unobserved.",
+        "We propose a new causal inference method that splits pre-treatment text data, infers two proxies from two zero-shot models on the separate splits, and applies these proxies in the proximal g-formula.",
+        "We prove that our text-based proxy method satisfies identification conditions required by the proximal g-formula while other seemingly reasonable proposals do not.",
+        "We evaluate our method in synthetic and semi-synthetic settings and find that it produces estimates with low bias.",
+        "This combination of proximal causal inference and zero-shot classifiers is novel (to our knowledge) and expands the set of text-specific causal methods available to practitioners."
+      ]
+    },
+    {
+      "title": "RCT Rejection Sampling for Causal Estimation Evaluation",
+      "abstract": [
+        "Confounding is a significant obstacle to unbiased estimation of causal effects from observational data.",
+        "For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- researchers have proposed methods to adjust for confounding by adapting machine learning methods to the goal of causal estimation.",
+        "However, empirical evaluation of these adjustment methods has been challenging and limited.",
+        "In this work, we build on a promising empirical evaluation strategy that simplifies evaluation design and uses real data: subsampling randomized controlled trials (RCTs) to create confounded observational datasets while using the average causal effects from the RCTs as ground-truth.",
+        "We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT.",
+        "Using synthetic data, we show our algorithm indeed results in low bias when oracle estimators are evaluated on the confounded samples, which is not always the case for a previously proposed algorithm.",
+        "In addition to this identification result, we highlight several finite data considerations for evaluation designers who plan to use RCT rejection sampling on their own datasets.",
+        "As a proof of concept, we implement an example evaluation pipeline and walk through these finite data considerations with a novel, real-world RCT -- which we release publicly -- consisting of approximately 70k observations and text data as high-dimensional covariates.",
+        "Together, these contributions build towards a broader agenda of improved empirical evaluation for causal estimation."
+      ]
+    },
+    {
+      "title": "Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications",
+      "abstract": [
+        "Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups.",
+        "In this work, we develop and validate an interpretable approach for measuring scholarly jargon from text.",
+        "Expanding the scope of prior work which focuses on word types, we use word sense induction to also identify words that are widespread but overloaded with different meanings across fields.",
+        "We then estimate the prevalence of these discipline-specific words and senses across hundreds of subfields, and show that word senses provide a complementary, yet unique view of jargon alongside word types.",
+        "We demonstrate the utility of our metrics for science of science and computational sociolinguistics by highlighting two key social implications.",
+        "First, though most fields reduce their use of jargon when writing for general-purpose venues, and some fields (e.g., biological sciences) do so less than others.",
+        "Second, the direction of correlation between jargon and citation rates varies among fields, but jargon is nearly always negatively correlated with interdisciplinary impact.",
+        "Broadly, our findings suggest that though multidisciplinary venues intend to cater to more general audiences, some fields' writing norms may act as barriers rather than bridges, and thus impede the dispersion of scholarly ideas."
+      ]
+    },
+    {
+      "title": "Paying Attention to the Algorithm Behind the Curtain: Bringing Transparency to YouTube's Demonetization Algorithms",
+      "abstract": [
+        "YouTube has long been a top-choice destination for independent video content creators to share their work.",
+        "A large part of YouTube's appeal is owed to its practice of sharing advertising revenue with qualifying content creators through the YouTube Partner Program (YPP).",
+        "In recent years, changes to the monetization policies and the introduction of algorithmic systems for making monetization decisions have been a source of controversy and tension between content creators and the platform.",
+        "There have been numerous accusations suggesting that the underlying monetization algorithms engage in preferential treatment of larger channels and effectively censor minority voices by demonetizing their content.",
+        "In this paper, we conduct a measurement of the YouTube monetization algorithms.",
+        "We begin by measuring the incidence rates of different monetization decisions and the time taken to reach them.",
+        "Next, we analyze the relationships between video content, channel popularity and these decisions.",
+        "Finally, we explore the relationship between demonetization and a channel's view growth rate.",
+        "Taken all together, our work suggests that demonetization after a video is publicly listed is not a common occurrence, the characteristics of the process are associated with channel size and (in unexplainable ways) video topic, and demonetization appears to have a harsh influence on the growth rate of smaller channels.",
+        "We also highlight the challenges associated with conducting large-scale algorithm audits such as ours and make an argument for more transparency in algorithmic decision-making."
+      ]
+    },
+    {
+      "title": "Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond",
+      "abstract": [
+        "Abstract A fundamental goal of scientific research is to learn about causal relationships.",
+        "However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks.",
+        "This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing.",
+        "Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties.",
+        "In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape.",
+        "We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding.",
+        "In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models.",
+        "We thus provide a unified overview of causal inference for the NLP community.1"
+      ]
+    },
+    {
+      "title": "Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence",
+      "abstract": [
+        "Automated event extraction in social science applications often requires corpus-level evaluations: for example, aggregating text predictions across metadata and unbiased estimates of recall.",
+        "We combine corpus-level evaluation requirements with a real-world, social science setting and introduce the IndiaPoliceEvents corpus--all 21,391 sentences from 1,257 English-language Times of India articles about events in the state of Gujarat during March 2002.",
+        "Our trained annotators read and label every document for mentions of police activity events, allowing for unbiased recall evaluations.",
+        "In contrast to other datasets with structured event representations, we gather annotations by posing natural questions, and evaluate off-the-shelf models for three different tasks: sentence classification, document ranking, and temporal aggregation of target events.",
+        "We present baseline results from zero-shot BERT-based models fine-tuned on natural language inference and passage retrieval tasks.",
+        "Our novel corpus-level evaluations and annotation approach can guide creation of similar social-science-oriented resources in the future."
+      ]
+    },
+    {
+      "title": "Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects",
+      "abstract": [
+        "Using observed language to understand interpersonal interactions is important in high-stakes decision making.",
+        "We propose a causal research design for observational (non-experimental) data to estimate the natural direct and indirect effects of social group signals (e.g. race or gender) on speakers\u2019 responses with separate aspects of language as causal mediators.",
+        "We illustrate the promises and challenges of this framework via a theoretical case study of the effect of an advocate\u2019s gender on interruptions from justices during U.S. Supreme Court oral arguments.",
+        "We also discuss challenges conceptualizing and operationalizing causal variables such as gender and language that comprise of many components, and we articulate technical open challenges such as temporal dependence between language mediators in conversational settings."
+      ]
+    },
+    {
+      "title": "Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates",
+      "abstract": [
+        "Many applications of computational social science aim to infer causal conclusions from non-experimental data.",
+        "Such observational data often contains confounders, variables that influence both potential causes and potential effects.",
+        "Unmeasured or latent confounders can bias causal estimates, and this has motivated interest in measuring potential confounders from observed text.",
+        "For example, an individual\u2019s entire history of social media posts or the content of a news article could provide a rich measurement of multiple confounders.",
+        "Yet, methods and applications for this problem are scattered across different communities and evaluation practices are inconsistent.",
+        "This review is the first to gather and categorize these examples and provide a guide to data-processing and evaluation decisions.",
+        "Despite increased attention on adjusting for confounding using text, there are still many open problems, which we highlight in this paper."
+      ]
+    },
+    {
+      "title": "Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty",
+      "abstract": [
+        "Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data.",
+        "In this paper, we examine one such text-as-data application, an established economic index that measures economic policy uncertainty from keyword occurrences in news.",
+        "This index, which is shown to correlate with firm investment, employment, and excess market returns, has had substantive impact in both the private sector and academia.",
+        "Yet, as we revisit and extend the original authors\u2019 annotations and text measurements we find interesting text-as-data methodological research questions: (1) Are annotator disagreements a reflection of ambiguity in language? (",
+        "2) Do alternative text measurements correlate with one another and with measures of external predictive validity?",
+        "We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index."
+      ]
+    },
+    {
+      "title": "Modeling Financial Analysts\u2019 Decision Making via the Pragmatics and Semantics of Earnings Calls",
+      "abstract": [
+        "Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts.",
+        "After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions.",
+        "In this paper, we examine analysts\u2019 decision making behavior as it pertains to the language content of earnings calls.",
+        "We identify a set of 20 pragmatic features of analysts\u2019 questions which we correlate with analysts\u2019 pre-call investor recommendations.",
+        "We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts\u2019 post-call changes in price targets.",
+        "Our results show that earnings calls are moderately predictive of analysts\u2019 decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions.",
+        "A breakdown of model errors indicates disparate performance on calls from different market sectors."
+      ]
+    },
+    {
+      "title": "Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses",
+      "abstract": [
+        "Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions.",
+        "However, ambiguity is inherent to natural language syntax, and communicating such ambiguity is important for error analysis and better-informed downstream applications.",
+        "In this work, we propose a transition sampling algorithm to sample from the full joint distribution of parse trees defined by a transition-based parsing model, and demonstrate the use of the samples in probabilistic dependency analysis.",
+        "First, we define the new task of dependency path prediction, inferring syntactic substructures over part of a sentence, and provide the first analysis of performance on this task.",
+        "Second, we demonstrate the usefulness of our Monte Carlo syntax marginal method for parser error analysis and calibration.",
+        "Finally, we use this method to propagate parse uncertainty to two downstream information extraction applications: identifying persons killed by police and semantic role assignment."
+      ]
+    },
+    {
+      "title": "Uncertainty-aware generative models for inferring document class prevalence",
+      "abstract": [
+        "Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled examples in a group\u2014for example, the proportion of a document collection with positive sentiment.",
+        "Previous work has focused on aggregating and adjusting discriminative individual classifiers to obtain prevalence point estimates.",
+        "But imperfect classifier accuracy ought to be reflected in uncertainty over the predicted prevalence for scientifically valid inference.",
+        "In this work, we present (1) a generative probabilistic modeling approach to prevalence estimation, and (2) the construction and evaluation of prevalence confidence intervals; in particular, we demonstrate that an off-the-shelf discriminative classifier can be given a generative re-interpretation, by backing out an implicit individual-level likelihood function, which can be used to conduct fast and simple group-level Bayesian inference.",
+        "Empirically, we demonstrate our approach provides better confidence interval coverage than an alternative, and is dramatically more robust to shifts in the class prior between training and testing."
+      ]
+    },
+    {
+      "title": "Identifying civilians killed by police with distantly supervised entity-event extraction",
+      "abstract": [
+        "We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police.",
+        "We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers.",
+        "Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases."
+      ]
+    }
+  ],
+  "user_kps": [
+    "academic language",
+    "analysts",
+    "annotated corpus",
+    "bibliometrics",
+    "causal inferences",
+    "causal learning",
+    "causal semantics",
+    "citation-based indicators",
+    "collective inference",
+    "confounding bias",
+    "conversational participants",
+    "dependency parsing",
+    "event extraction",
+    "event summarization",
+    "lexicons",
+    "linguistic cues",
+    "monetization",
+    "sentence models",
+    "text-based analysis",
+    "youtube"
+  ]
+}