Rachel Rudinger


2024

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Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering
Neha Srikanth | Rupak Sarkar | Heran Mane | Elizabeth Aparicio | Quynh Nguyen | Rachel Rudinger | Jordan Boyd-Graber
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions. In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study assumptions and implications, or pragmatic inferences, made when mothers ask questions about pregnancy and infant care by collecting a dataset of 2,727 inferences from 500 questions across three diverse sources. We study how health experts naturally address these inferences when writing answers, and illustrate that informing existing QA pipelines with pragmatic inferences produces responses that are more complete, mitigating the propagation of harmful beliefs.

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PropBank-Powered Data Creation: Utilizing Sense-Role Labelling to Generate Disaster Scenario Data
Mollie Frances Shichman | Claire Bonial | Taylor A. Hudson | Austin Blodgett | Francis Ferraro | Rachel Rudinger
Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024

For human-robot dialogue in a search-and-rescue scenario, a strong knowledge of the conditions and objects a robot will face is essential for effective interpretation of natural language instructions. In order to utilize the power of large language models without overwhelming the limited storage capacity of a robot, we propose PropBank-Powered Data Creation. PropBank-Powered Data Creation is an expert-in-the-loop data generation pipeline which creates training data for disaster-specific language models. We leverage semantic role labeling and Rich Event Ontology resources to efficiently develop seed sentences for fine-tuning a smaller, targeted model that could operate onboard a robot for disaster relief. We developed 32 sentence templates, which we used to make 2 seed datasets of 175 instructions for earthquake search and rescue and train derailment response. We further leverage our seed datasets as evaluation data to test our baseline fine-tuned models.

2023

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FORK: A Bite-Sized Test Set for Probing Culinary Cultural Biases in Commonsense Reasoning Models
Shramay Palta | Rachel Rudinger
Findings of the Association for Computational Linguistics: ACL 2023

It is common sense that one should prefer to eat a salad with a fork rather than with a chainsaw. However, for eating a bowl of rice, the choice between a fork and a pair of chopsticks is culturally relative. We introduce FORK, a small, manually-curated set of CommonsenseQA-style questions for probing cultural biases and assumptions present in commonsense reasoning systems, with a specific focus on food-related customs. We test several CommonsenseQA systems on FORK, and while we see high performance on questions about the US culture, the poor performance of these systems on questions about non-US cultures highlights systematic cultural assumptions aligned with US over non-US cultures.

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SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models
Haozhe An | Zongxia Li | Jieyu Zhao | Rachel Rudinger
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

A common limitation of diagnostic tests for detecting social biases in NLP models is that they may only detect stereotypic associations that are pre-specified by the designer of the test. Since enumerating all possible problematic associations is infeasible, it is likely these tests fail to detect biases that are present in a model but not pre-specified by the designer. To address this limitation, we propose SODAPOP (SOcial bias Discovery from Answers about PeOPle), an approach for automatic social bias discovery in social commonsense question-answering. The SODAPOP pipeline generates modified instances from the Social IQa dataset (Sap et al., 2019b) by (1) substituting names associated with different demographic groups, and (2) generating many distractor answers from a masked language model. By using a social commonsense model to score the generated distractors, we are able to uncover the model’s stereotypic associations between demographic groups and an open set of words. We also test SODAPOP on debiased models and show the limitations of multiple state-of-the-art debiasing algorithms.

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Use Defines Possibilities: Reasoning about Object Function to Interpret and Execute Robot Instructions
Mollie Shichman | Claire Bonial | Austin Blodgett | Taylor Hudson | Francis Ferraro | Rachel Rudinger
Proceedings of the 15th International Conference on Computational Semantics

Language models have shown great promise in common-sense related tasks. However, it remains unseen how they would perform in the context of physically situated human-robot interactions, particularly in disaster-relief sce- narios. In this paper, we develop a language model evaluation dataset with more than 800 cloze sentences, written to probe for the func- tion of over 200 objects. The sentences are divided into two tasks: an “easy” task where the language model has to choose between vo- cabulary with different functions (Task 1), and a “challenge” where it has to choose between vocabulary with the same function, yet only one vocabulary item is appropriate given real world constraints on functionality (Task 2). Dis- tilBERT performs with about 80% accuracy for both tasks. To investigate how annotator variability affected those results, we developed a follow-on experiment where we compared our original results with wrong answers chosen based on embedding vector distances. Those results showed increased precision across docu- ments but a 15% decrease in accuracy. We con- clude that language models do have a strong knowledge basis for object reasoning, but will require creative fine-tuning strategies in order to be successfully deployed.

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What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
Abhilasha Sancheti | Aparna Garimella | Balaji Srinivasan | Rachel Rudinger
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the key rights and duties requiring review vary for each contracting party. In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties. To facilitate this, we curate a dataset comprising of party-specific pairwise importance comparisons annotated by legal experts, covering ~293K sentence pairs that include obligations, entitlements, and prohibitions extracted from lease agreements. Using this dataset, we train a pairwise importance ranker and propose a pipeline-based extractive summarization system that generates a party-specific contract summary. We establish the need for incorporating domain-specific notions of importance during summarization by comparing our system against various baselines using both automatic and human evaluation methods.

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Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases
Haozhe An | Rachel Rudinger
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Through the use of first name substitution experiments, prior research has demonstrated the tendency of social commonsense reasoning models to systematically exhibit social biases along the dimensions of race, ethnicity, and gender (An et al., 2023). Demographic attributes of first names, however, are strongly correlated with corpus frequency and tokenization length, which may influence model behavior independent of or in addition to demographic factors. In this paper, we conduct a new series of first name substitution experiments that measures the influence of these factors while controlling for the others. We find that demographic attributes of a name (race, ethnicity, and gender) and name tokenization length are both factors that systematically affect the behavior of social commonsense reasoning models.

2022

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Agent-Specific Deontic Modality Detection in Legal Language
Abhilasha Sancheti | Aparna Garimella | Balaji Vasan Srinivasan | Rachel Rudinger
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties. While natural language understanding technologies can be valuable in supporting such understanding in the legal domain, the limited availability of datasets annotated for deontic modalities in the legal domain, due to the cost of hiring experts and privacy issues, is a bottleneck. To this end, we introduce, LEXDEMOD, a corpus of English contracts annotatedwith deontic modality expressed with respect to a contracting party or agent along with the modal triggers. We benchmark this dataset on two tasks: (i) agent-specific multi-label deontic modality classification, and (ii) agent-specific deontic modality and trigger span detection using Transformer-based (Vaswani et al., 2017) language models. Transfer learning experiments show that the linguistic diversity of modal expressions in LEXDEMOD generalizes reasonably from lease to employment andrental agreements. A small case study indicates that a model trained on LEXDEMOD can detect red flags with high recall. We believe our work offers a new research direction for deontic modality detection in the legal domain.

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Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)
Antoine Bosselut | Xiang Li | Bill Yuchen Lin | Vered Shwartz | Bodhisattwa Prasad Majumder | Yash Kumar Lal | Rachel Rudinger | Xiang Ren | Niket Tandon | Vilém Zouhar
Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)

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Human Schema Curation via Causal Association Rule Mining
Noah Weber | Anton Belyy | Nils Holzenberger | Rachel Rudinger | Benjamin Van Durme
Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022

Event schemas are structured knowledge sources defining typical real-world scenarios (e.g., going to an airport). We present a framework for efficient human-in-the-loop construction of a schema library, based on a novel script induction system and a well-crafted interface that allows non-experts to “program” complex event structures. Associated with this work we release a schema library: a machine readable resource of 232 detailed event schemas, each of which describe a distinct typical scenario in terms of its relevant sub-event structure (what happens in the scenario), participants (who plays a role in the scenario), fine-grained typing of each participant, and the implied relational constraints between them. We make our schema library and the SchemaBlocks interface available online.

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What do Large Language Models Learn about Scripts?
Abhilasha Sancheti | Rachel Rudinger
Proceedings of the 11th Joint Conference on Lexical and Computational Semantics

Script Knowledge (Schank and Abelson, 1975) has long been recognized as crucial for language understanding as it can help in filling in unstated information in a narrative. However, such knowledge is expensive to produce manually and difficult to induce from text due to reporting bias (Gordon and Van Durme, 2013). In this work, we are interested in the scientific question of whether explicit script knowledge is present and accessible through pre-trained generative language models (LMs). To this end, we introduce the task of generating full event sequence descriptions (ESDs) given a scenario as a natural language prompt. Through zero-shot probing, we find that generative LMs produce poor ESDs with mostly omitted, irrelevant, repeated or misordered events. To address this, we propose a pipeline-based script induction framework (SIF) which can generate good quality ESDs for unseen scenarios (e.g., bake a cake). SIF is a two-staged framework that fine-tunes LM on a small set of ESD examples in the first stage. In the second stage, ESD generated for an unseen scenario is post-processed using RoBERTa-based models to filter irrelevant events, remove repetitions, and reorder the temporally misordered events. Through automatic and manual evaluations, we demonstrate that SIF yields substantial improvements (1-3 BLEU points) over a fine-tuned LM. However, manual analysis shows that there is great room for improvement, offering a new research direction for inducing script knowledge.

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Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
Yang Trista Cao | Anna Sotnikova | Hal Daumé III | Rachel Rudinger | Linda Zou
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.

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Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution
Connor Baumler | Rachel Rudinger
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

As using they/them as personal pronouns becomes increasingly common in English, it is important that coreference resolution systems work as well for individuals who use personal “they” as they do for those who use gendered personal pronouns. We introduce a new benchmark for coreference resolution systems which evaluates singular personal “they” recognition. Using these WinoNB schemas, we evaluate a number of publicly available coreference resolution systems and confirm their bias toward resolving “they” pronouns as plural.

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Partial-input baselines show that NLI models can ignore context, but they don’t.
Neha Srikanth | Rachel Rudinger
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations. We investigate whether state-of-the-art NLI models are capable of overriding default inferences made by a partial-input baseline. We introduce an evaluation set of 600 examples consisting of perturbed premises to examine a RoBERTa model’s sensitivity to edited contexts. Our results indicate that NLI models are still capable of learning to condition on context—a necessary component of inferential reasoning—despite being trained on artifact-ridden datasets.

2021

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Analyzing Stereotypes in Generative Text Inference Tasks
Anna Sotnikova | Yang Trista Cao | Hal Daumé III | Rachel Rudinger
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain
Christine Herlihy | Rachel Rudinger
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance (CITATION). We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts (CITATION). We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on the difficult subset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.

2020

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Thinking Like a Skeptic: Defeasible Inference in Natural Language
Rachel Rudinger | Vered Shwartz | Jena D. Hwang | Chandra Bhagavatula | Maxwell Forbes | Ronan Le Bras | Noah A. Smith | Yejin Choi
Findings of the Association for Computational Linguistics: EMNLP 2020

Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin). Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning. We introduce Defeasible NLI (abbreviated 𝛿-NLI), a dataset for defeasible inference in natural language. Defeasible NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms. From Defeasible NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging. Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68% of the time.

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The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White | Elias Stengel-Eskin | Siddharth Vashishtha | Venkata Subrahmanyan Govindarajan | Dee Ann Reisinger | Tim Vieira | Keisuke Sakaguchi | Sheng Zhang | Francis Ferraro | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification—with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at http://decomp.io.

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“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models
Vered Shwartz | Rachel Rudinger | Oyvind Tafjord
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models. We focus on artifacts associated with the representation of given names (e.g., Donald), which, depending on the corpus, may be associated with specific entities, as indicated by next token prediction (e.g., Trump). While helpful in some contexts, grounding happens also in under-specified or inappropriate contexts. For example, endings generated for ‘Donald is a’ substantially differ from those of other names, and often have more-than-average negative sentiment. We demonstrate the potential effect on downstream tasks with reading comprehension probes where name perturbation changes the model answers. As a silver lining, our experiments suggest that additional pre-training on different corpora may mitigate this bias.

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Causal Inference of Script Knowledge
Noah Weber | Rachel Rudinger | Benjamin Van Durme
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.

2019

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On Measuring Social Biases in Sentence Encoders
Chandler May | Alex Wang | Shikha Bordia | Samuel R. Bowman | Rachel Rudinger
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test’s assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders.

2018

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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak | Aparajita Haldar | Rachel Rudinger | J. Edward Hu | Ellie Pavlick | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources.

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Hypothesis Only Baselines in Natural Language Inference
Adam Poliak | Jason Naradowsky | Aparajita Haldar | Rachel Rudinger | Benjamin Van Durme
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.

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Neural Models of Factuality
Rachel Rudinger | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.

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Gender Bias in Coreference Resolution
Rachel Rudinger | Jason Naradowsky | Brian Leonard | Benjamin Van Durme
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these “Winogender schemas,” we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.

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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak | Aparajita Haldar | Rachel Rudinger | J. Edward Hu | Ellie Pavlick | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.

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Neural-Davidsonian Semantic Proto-role Labeling
Rachel Rudinger | Adam Teichert | Ryan Culkin | Sheng Zhang | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.

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Cross-lingual Decompositional Semantic Parsing
Sheng Zhang | Xutai Ma | Rachel Rudinger | Kevin Duh | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.

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Lexicosyntactic Inference in Neural Models
Aaron Steven White | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.

2017

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Social Bias in Elicited Natural Language Inferences
Rachel Rudinger | Chandler May | Benjamin Van Durme
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.

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Skip-Prop: Representing Sentences with One Vector Per Proposition
Rachel Rudinger | Kevin Duh | Benjamin Van Durme
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Short papers

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An Evaluation of PredPatt and Open IE via Stage 1 Semantic Role Labeling
Sheng Zhang | Rachel Rudinger | Benjamin Van Durme
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Short papers

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Ordinal Common-sense Inference
Sheng Zhang | Rachel Rudinger | Kevin Duh | Benjamin Van Durme
Transactions of the Association for Computational Linguistics, Volume 5

Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.

2016

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Universal Decompositional Semantics on Universal Dependencies
Aaron Steven White | Drew Reisinger | Keisuke Sakaguchi | Tim Vieira | Sheng Zhang | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Script Induction as Language Modeling
Rachel Rudinger | Pushpendre Rastogi | Francis Ferraro | Benjamin Van Durme
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Semantic Proto-Roles
Drew Reisinger | Rachel Rudinger | Francis Ferraro | Craig Harman | Kyle Rawlins | Benjamin Van Durme
Transactions of the Association for Computational Linguistics, Volume 3

We present the first large-scale, corpus based verification of Dowty’s seminal theory of proto-roles. Our results demonstrate both the need for and the feasibility of a property-based annotation scheme of semantic relationships, as opposed to the currently dominant notion of categorical roles.

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Learning to predict script events from domain-specific text
Rachel Rudinger | Vera Demberg | Ashutosh Modi | Benjamin Van Durme | Manfred Pinkal
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2014

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Is the Stanford Dependency Representation Semantic?
Rachel Rudinger | Benjamin Van Durme
Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation

2013

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SenseSpotting: Never let your parallel data tie you to an old domain
Marine Carpuat | Hal Daumé III | Katharine Henry | Ann Irvine | Jagadeesh Jagarlamudi | Rachel Rudinger
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)