Papers by Nianwen Xue
Transition-Based Chinese AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation where the meaning of a sentence is encoded as a rooted, directed and acyclic graph. |
| Approach: | They propose a transition-based AMR parsing framework for Chinese to be used in the next generation of AMR. |
| Outcome: | The proposed parser is based on the Chinese AMR bank. |
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection (2026.findings-acl)
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| Challenge: | Weighted majority voting requires a critic to evaluate each candidate’s reasoning trace to produce the answer’s confidence score. |
| Approach: | They propose a lightweight framework that uses a measure of semantic similarity to filter reasoning traces that are semantically equivalent to others, degenerate, or hallucinated. |
| Outcome: | The proposed framework reduces token usage by 47% while maintaining or exceeding the accuracy of CISC. |
Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)
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| Challenge: | a recent study examined the effects of media framing on public perception and understanding of news articles. |
| Approach: | They propose to extract framing devices employed by media to assess their role in framating the narrative. |
| Outcome: | The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects. |
A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches (2023.eacl-main)
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| Challenge: | Lexical and grammatical aspect plays essential roles in semantic interpretation, but many systems do not address it systematically. |
| Approach: | They propose to model lexical and grammatical aspect using computational approaches . they argue that a good computational understanding of lexic and grammmatical aspects is needed . |
| Outcome: | The proposed models are based on the lexical and grammatical aspect of a situation, the authors argue . they argue that the models need to be able to handle and evaluate the aspect systematically . |
Modal Dependency Parsing as Structured Prediction over Source-Cue Scope (2026.acl-long)
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| Challenge: | Existing work on identifying sources only focuses on defining source-introducing cues . a structured model focuses learning at the source-cue level and constrains event-level decisions to a small, scope-defined candidate set. |
| Approach: | They propose a framework that leverages large language models to explicitly identify source-cue pairs and their respective scope to define modal contexts. |
| Outcome: | The proposed framework surpasses state-of-the-art results by 3 and 4% for English and Chinese datasets. |
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech (2021.acl-long)
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| Challenge: | Existing work regards dropped pronoun recovery and conversational discourse parsing as two separate tasks and tackles them separately. |
| Approach: | They propose a neural model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a new dataset . the proposed model is based on linguistic and semantic information from Chinese conversational speech . |
Modal Dependency Parsing via Language Model Priming (2022.naacl-main)
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| Challenge: | modal dependency parsing is a task of parse a text into its modal dependence structure . the root node of an MDS is always the author of a document, the ultimate source of information sources . |
| Approach: | They propose a modal dependency parser based on priming pre-trained language models and evaluate it on two data sets. |
| Outcome: | The proposed parser improves on two data sets. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
Modal Dependency Parsing via Biaffine Attention with Self-Loop (2025.findings-acl)
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| Challenge: | A modal dependency structure is a web of connections between events and sources of information in a document that allows for tracing of who-said-what with what levels of certainty. |
| Approach: | They propose a modal dependency structure that integrates biaffine attention with a large language model to optimize against domain-specific challenges of modal dependence parsing. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on English and Chinese datasets by 2% and 4% respectively. |
Factuality Assessment as Modal Dependency Parsing (2021.acl-long)
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| Challenge: | a critical step towards factuality assessment is to determine the factuality of events in text. |
| Approach: | They propose a modal dependency parsing task that assesses the factuality of events in text . they crowdsource a large-scale data set annotated with modal dependence structures . |
| Outcome: | The proposed model outperforms the pipeline model in factuality assessment . the proposed model is based on a crowdsourced dataset . |
Seeing the Same Story Differently: Framing‐Divergent Event Coreference for Computational Framing Analysis (2025.emnlp-main)
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| Challenge: | a new task aims to capture subtle differences in how news articles frame events . a central challenge is capturing how same real-world event can evolve into sharply divergent narratives . |
| Approach: | They propose a task that identifies pairs of event mentions referring to the same underlying occurrence but differing in framing across documents. |
| Outcome: | The proposed method enables scalable, interpretable analysis of how media frame the same events differently. |
Annotating Temporal Dependency Graphs via Crowdsourcing (2020.emnlp-main)
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| Challenge: | Existing temporal annotation schemes have been limited due to the complexity of temporal relations between events. |
| Approach: | They propose to build a corpus of Wikinews articles annotated with temporal dependency graphs . they also propose a crowdsourcing strategy to annotate TDGs based on the corpus . |
| Outcome: | The proposed method achieves a good trade-off between completeness and practicality in temporal annotation. |
Beyond Benchmarks: Building a Richer Cross-Document Event Coreference Dataset with Decontextualization (2025.naacl-long)
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| Challenge: | Existing datasets for Cross-Document Event Coreference (CDEC) are small and lacking diversity. |
| Approach: | They propose a new approach leveraging large language models to decontextualize event mentions by simplifying the document-level annotation task to sentence pairs with enriched context. |
| Outcome: | The proposed approach improves the quality of the dataset and generalizability of the model. |
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields (2020.findings-emnlp)
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| Challenge: | Existing approaches to recover dropped pronouns ignore the dependencies between pronounes in neighboring utterances. |
| Approach: | They propose a framework that combines Transformer network and General Conditional Random Fields to model the dependencies between pronouns in neighboring utterances. |
| Outcome: | The proposed framework outperforms state-of-the-art models on three Chinese conversation datasets showing that it captures the dependencies between pronouns in neighboring utterances. |
Neural Ranking Models for Temporal Dependency Structure Parsing (D18-1)
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| Challenge: | a new neural temporal dependency parser is being developed for news reports and narrative stories . a similar system is used for other NLP applications such as timeline construction . |
| Approach: | They build a neural temporal dependency parser that parses time expressions and events in a text . their results shed light on the nature of temporal relation structures in different domains . |
| Outcome: | The proposed model beats baselines on news reports and narrative stories on two data domains. |
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19 (2021.emnlp-demo)
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| Challenge: | a new machine reading system ingests open-source text documents to analyze COVID-19 events . the system extracts COVId-19 related events and relations between them . |
| Approach: | They propose a machine reading system that ingests open-source text documents and extracts COVID-19 related events and relations between them. |
| Outcome: | The proposed system extracts COVID-19 related events and relations from open-source text . it will help government agencies alleviate the information overload and respond to COVId-19 . |
Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a sentencelevel formalism designed for English. |
| Approach: | They present an intuitive tool for evaluating graph-based meaning representations . they use an anchor broadcast alignment algorithm that is not subject to local maxima . |
| Outcome: | The proposed tool is highly correlated with the widely used Smatch score, but computation takes only about 40% the time. |
Recovering dropped pronouns in Chinese conversations via modeling their referents (N19-1)
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| Challenge: | Pronouns are often dropped in conversational genres as their referents can be easily understood from context. |
| Approach: | They propose an end-to-end neural network model to recover dropped pronouns in conversational data. |
| Outcome: | The proposed model improves on three different conversational genres. |
Reframing Responsibility: Framing-Aware Event Causality Identification (2026.acl-long)
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| Challenge: | Causal explanations in political narratives are often framed and contested. |
| Approach: | They propose a framing-aware extension of ECI that models causal explanations as structured claims including responsibility targets, evaluative frams, source type, and epistemic modality. |
| Outcome: | The proposed model enables quantitative analysis of divergent causal attribution across narratives. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations (2021.emnlp-demo)
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| Challenge: | Uniform Meaning Representations (UMRs) are graph-based semantic representations that can be used to annotate text. |
| Approach: | They present a web-based application for annotating Uniform Meaning Representations (UMR) they propose to use a graph-based cross-linguistically applicable semantic representation to annotate sentences and documents. |
| Outcome: | The proposed tool is based on a graph-based, cross-linguistically applicable semantic representation that can be used to annotate text. |
AnCast++: Document-Level Evaluation of Graph-based Meaning Representations (2025.findings-acl)
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| Challenge: | Abstract Meaning Representation (UMR) is a cross-lingual document-level graph-based representation that extends it to document- level semantic annotations. |
| Approach: | They propose an evaluation metric that unifies evaluation of four distinct sub-structures of UMR. |
| Outcome: | The proposed metric is made available on Github. |
Structured Interpretation of Temporal Relations (L18-1)
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| Challenge: | Temporal relations between events and time expressions are often modeled in an unstructured manner, resulting in inconsistent and incomplete annotation and computational modeling. |
| Approach: | They propose an annotation approach where events and time expressions form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora. |
| Outcome: | The proposed approach annotates 235 documents in news and narratives with 48 doubly annotated documents. |