Jena D. Hwang


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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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Sprucing up Supersenses: Untangling the Semantic Clusters of Accompaniment and Purpose
Jena D. Hwang | Nathan Schneider | Vivek Srikumar
Proceedings of the 14th Linguistic Annotation Workshop

We reevaluate an existing adpositional annotation scheme with respect to two thorny semantic domains: accompaniment and purpose. ‘Accompaniment’ broadly speaking includes two entities situated together or participating in the same event, while ‘purpose’ broadly speaking covers the desired outcome of an action, the intended use or evaluated use of an entity, and more. We argue the policy in the SNACS scheme for English should be recalibrated with respect to these clusters of interrelated meanings without adding complexity to the overall scheme. Our analysis highlights tradeoffs in lumping vs. splitting decisions as well as the flexibility afforded by the construal analysis.

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K-SNACS: Annotating Korean Adposition Semantics
Jena D. Hwang | Hanwool Choe | Na-Rae Han | Nathan Schneider
Proceedings of the Second International Workshop on Designing Meaning Representations

While many languages use adpositions to encode semantic relationships between content words in a sentence (e.g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning. In this paper, we empirically adapt the SNACS framework (Schneider et al., 2018) to Korean, a language that is typologically distant from English—the language SNACS was based on. We apply the SNACS framework to annotate the highly popular novellaThe Little Prince with semantic supersense labels over allKorean postpositions. Thus, we introduce the first broad-coverage corpus annotated with Korean postposition semantics and provide a detailed analysis of the corpus with an apples-to-apples comparison between Korean and English annotations

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Social Chemistry 101: Learning to Reason about Social and Moral Norms
Maxwell Forbes | Jena D. Hwang | Vered Shwartz | Maarten Sap | Yejin Choi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Social norms—the unspoken commonsense rules about acceptable social behavior—are crucial in understanding the underlying causes and intents of people’s actions in narratives. For example, underlying an action such as “wanting to call cops on my neighbor” are social norms that inform our conduct, such as “It is expected that you report crimes.” We present SOCIAL CHEMISTRY, a new conceptual formalism to study people’s everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language. We introduce SOCIAL-CHEM-101, a large-scale corpus that catalogs 292k rules-of-thumb such as “It is rude to run a blender at 5am” as the basic conceptual units. Each rule-of-thumb is further broken down with 12 different dimensions of people’s judgments, including social judgments of good and bad, moral foundations, expected cultural pressure, and assumed legality, which together amount to over 4.5 million annotations of categorical labels and free-text descriptions. Comprehensive empirical results based on state-of-the-art neural models demonstrate that computational modeling of social norms is a promising research direction. Our model framework, Neural Norm Transformer, learns and generalizes SOCIAL-CHEM-101 to successfully reason about previously unseen situations, generating relevant (and potentially novel) attribute-aware social rules-of-thumb.

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Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Lianhui Qin | Vered Shwartz | Peter West | Chandra Bhagavatula | Jena D. Hwang | Ronan Le Bras | Antoine Bosselut | Yejin Choi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs) can be challenging, as they are trained either to condition only on the past context or to perform narrowly scoped text-infilling. In this paper, we propose DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision. The key intuition of our algorithm is incorporating the future through back-propagation, during which, we only update the internal representation of the output while fixing the model parameters. By alternating between forward and backward propagation, DeLorean can decode the output representation that reflects both the left and right contexts. We demonstrate that our approach is general and applicable to two nonmonotonic reasoning tasks: abductive text generation and counterfactual story revision, where DeLorean outperforms a range of unsupervised and some supervised methods, based on automatic and human evaluation.

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Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
Tae Hwan Oh | Ji Yoon Han | Hyonsu Choe | Seokwon Park | Han He | Jinho D. Choi | Na-Rae Han | Jena D. Hwang | Hansaem Kim
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word- order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0% in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.


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Preparing SNACS for Subjects and Objects
Adi Shalev | Jena D. Hwang | Nathan Schneider | Vivek Srikumar | Omri Abend | Ari Rappoport
Proceedings of the First International Workshop on Designing Meaning Representations

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants—including subjects and objects—directly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.


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Building Universal Dependency Treebanks in Korean
Jayeol Chun | Na-Rae Han | Jena D. Hwang | Jinho D. Choi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Comprehensive Supersense Disambiguation of English Prepositions and Possessives
Nathan Schneider | Jena D. Hwang | Vivek Srikumar | Jakob Prange | Austin Blodgett | Sarah R. Moeller | Aviram Stern | Adi Bitan | Omri Abend
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic relations are often signaled with prepositional or possessive marking—but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker’s lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.

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Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
Agata Savary | Carlos Ramisch | Jena D. Hwang | Nathan Schneider | Melanie Andresen | Sameer Pradhan | Miriam R. L. Petruck
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

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Coordinate Structures in Universal Dependencies for Head-final Languages
Hiroshi Kanayama | Na-Rae Han | Masayuki Asahara | Jena D. Hwang | Yusuke Miyao | Jinho D. Choi | Yuji Matsumoto
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean. UD applies a strict principle that makes the head of coordination the left-most conjunct. However, the guideline may produce syntactic trees which are difficult to accept in head-final languages. This paper describes the status in the current Japanese and Korean corpora and proposes alternative designs suitable for these languages.


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Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions
Jena D. Hwang | Archna Bhatia | Na-Rae Han | Tim O’Gorman | Vivek Srikumar | Nathan Schneider
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that an adposition’s lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition’s lexical function so they can be annotated at scale—supporting automatic, statistical processing of domain-general language—and discuss how this representation would allow for a simpler inventory of labels.


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Crazy Mad Nutters: The Language of Mental Health
Jena D. Hwang | Kristy Hollingshead
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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A Corpus of Preposition Supersenses
Nathan Schneider | Jena D. Hwang | Vivek Srikumar | Meredith Green | Abhijit Suresh | Kathryn Conger | Tim O’Gorman | Martha Palmer
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)

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Applying Universal Dependency to the Arapaho Language
Irina Wagner | Andrew Cowell | Jena D. Hwang
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)


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A Hierarchy with, of, and for Preposition Supersenses
Nathan Schneider | Vivek Srikumar | Jena D. Hwang | Martha Palmer
Proceedings of The 9th Linguistic Annotation Workshop

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Identification of Caused Motion Construction
Jena D. Hwang | Martha Palmer
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics


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PropBank: Semantics of New Predicate Types
Claire Bonial | Julia Bonn | Kathryn Conger | Jena D. Hwang | Martha Palmer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This research focuses on expanding PropBank, a corpus annotated with predicate argument structures, with new predicate types; namely, noun, adjective and complex predicates, such as Light Verb Constructions. This effort is in part inspired by a sister project to PropBank, the Abstract Meaning Representation project, which also attempts to capture “who is doing what to whom” in a sentence, but does so in a way that abstracts away from syntactic structures. For example, alternate realizations of a ‘destroying’ event in the form of either the verb ‘destroy’ or the noun ‘destruction’ would receive the same Abstract Meaning Representation. In order for PropBank to reach the same level of coverage and continue to serve as the bedrock for Abstract Meaning Representation, predicate types other than verbs, which have previously gone without annotation, must be annotated. This research describes the challenges therein, including the development of new annotation practices that walk the line between abstracting away from language-particular syntactic facts to explore deeper semantics, and maintaining the connection between semantics and syntactic structures that has proven to be very valuable for PropBank as a corpus of training data for Natural Language Processing applications.

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Criteria for Identifying and Annotating Caused Motion Constructions in Corpus Data
Jena D. Hwang | Annie Zaenen | Martha Palmer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

While natural language processing performance has been improved through the recognition that there is a relationship between the semantics of the verb and the syntactic context in which the verb is realized, sentences where the verb does not conform to the expected syntax-semantic patterning behavior remain problematic. For example, in the sentence “The crowed laughed the clown off the stage”, a verb of non-verbal communication laugh is used in a caused motion construction and gains a motion entailment that is atypical given its inherent lexical semantics. This paper focuses on our efforts at defining the semantic types and varieties of caused motion constructions (CMCs) through an iterative annotation process and establishing annotation guidelines based on these criteria to aid in the production of a consistent and reliable annotation. The annotation will serve as training and test data for classifiers for CMCs, and the CMC definitions developed throughout this study will be used in extending VerbNet to handle representations of sentences in which a verb is used in a syntactic context that is atypical for its lexical semantics.


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Incorporating Coercive Constructions into a Verb Lexicon
Claire Bonial | Susan Windisch Brown | Jena D. Hwang | Christopher Parisien | Martha Palmer | Suzanne Stevenson
Proceedings of the ACL 2011 Workshop on Relational Models of Semantics


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Towards a Domain Independent Semantics: Enhancing Semantic Representation with Construction Grammar
Jena D. Hwang | Rodney D. Nielsen | Martha Palmer
Proceedings of the NAACL HLT Workshop on Extracting and Using Constructions in Computational Linguistics

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Identifying Assertions in Text and Discourse: The Presentational Relative Clause Construction
Cecily Jill Duffield | Jena D. Hwang | Laura A. Michaelis
Proceedings of the NAACL HLT Workshop on Extracting and Using Constructions in Computational Linguistics

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PropBank Annotation of Multilingual Light Verb Constructions
Jena D. Hwang | Archna Bhatia | Claire Bonial | Aous Mansouri | Ashwini Vaidya | Nianwen Xue | Martha Palmer
Proceedings of the Fourth Linguistic Annotation Workshop


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Criteria for the Manual Grouping of Verb Senses
Cecily Jill Duffield | Jena D. Hwang | Susan Windisch Brown | Dmitriy Dligach | Sarah E. Vieweg | Jenny Davis | Martha Palmer
Proceedings of the Linguistic Annotation Workshop