Papers by Yufang Hou
Employing Argumentation Knowledge Graphs for Neural Argument Generation (2021.acl-long)
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| Challenge: | Existing methods for generating arguments use end-to-end knowledge graphs or are controlled with respect to the argument's topic, aspects, or stance. |
| Approach: | They construct and populate three knowledge graphs and encode them into debate portals and relevant paragraphs from Wikipedia. |
| Outcome: | The proposed model produces arguments with superior quality than those generated without knowledge. |
Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies (2025.acl-long)
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Massimiliano Pronesti, Joao H Bettencourt-Silva, Paul Flanagan, Alessandra Pascale, Oisín Redmond, Anya Belz, Yufang Hou
| Challenge: | Systematic reviews are widely regarded as the gold standard in evidence-based medicine, heavily influencing medical decisions made by doctors, health authorities, and patients. |
| Approach: | They propose a retrieval-augmented generation framework to tackle the unique challenges of evidence extraction by leveraging forest plots from Cochrane systematic reviews. |
| Outcome: | The proposed framework outperforms existing methods by up to 10.3% in the F1 score on this task. |
AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation (2026.eacl-tutorials)
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| Challenge: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
| Approach: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
| Outcome: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
D2S: Document-to-Slide Generation Via Query-Based Text Summarization (2021.naacl-main)
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| Challenge: | Existing research efforts to automate the document-to-slide generation process face a critical challenge: no publicly available dataset for training and benchmarking. |
| Approach: | They propose a dataset SciDuet that gathers papers and their corresponding slides from recent years’ NLP and ML conferences. |
| Outcome: | The proposed system outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation. |
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models (2023.acl-long)
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| Challenge: | generative large language models (LLMs) are widely used but fine-tuned to improve performance on downstream applications leads to violations of model licenses, model theft, and copyright infringement. |
| Approach: | They propose to trace back the origin of a model trained to its pre-trained base model . they use different knowledge levels and attribution strategies to find out how the model was trained . |
| Outcome: | The proposed method can trace back 8 out of 10 fine tuned models with different knowledge levels and attribution strategies. |
A Position Paper on the Automatic Generation of Machine Learning Leaderboards (2025.emnlp-main)
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| Challenge: | Automated leaderboard generation is a tool for comparing prior work with a tabular overview of experimental results. |
| Approach: | They propose an automatic leaderboard generation framework to standardise how the task is defined. |
| Outcome: | The proposed framework standardises how the ALG task is defined and proposes new directions . the proposed framework includes recommendations for datasets and metrics that promote fair evaluation . |
Beyond Abstracts: A New Dataset, Prompt Design Strategy and Method for Biomedical Synthesis Generation (2024.acl-srw)
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| Challenge: | Existing methods to automate systematic reviews of papers are slow and incomplete . authors propose a new method to automating the systematic review process . |
| Approach: | They propose a method for automatic synthesis generation using a dataset and prompting-based method. |
| Outcome: | The proposed method improves the existing model and prompts the system to generate high-quality syntheses. |
Missci: Reconstructing Fallacies in Misrepresented Science (2024.acl-long)
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| Challenge: | False or misleading narratives spread rapidly on social networks, posing challenges for non-experts in discerning credible information. |
| Approach: | They propose a model for fallacious reasoning that focuses on implicit fallacies between relevant content and the inaccurate claim and requires models to verbalize the fallacious thinking in addition to classifying it. |
| Outcome: | The proposed model focuses on implicit fallacies between relevant content and the inaccurate claim and requires models to verbalize the fallacious reasoning in addition to classifying it. |
Know Who Your Friends Are: Understanding Social Connections from Unstructured Text (N18-5)
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Léa Deleris, Francesca Bonin, Elizabeth Daly, Stéphane Deparis, Yufang Hou, Charles Jochim, Yassine Lassoued, Killian Levacher
| Challenge: | Having an understanding of interpersonal relationships is helpful in many contexts. |
| Approach: | They propose a system that extracts qualitative and quantitative information from texts and aggregates it to provide a condensed view of relationships. |
| Outcome: | The proposed system extracts qualitative and quantitative information elements about interactions and aggregates those to provide a condensed view of relationships. |
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks (2021.findings-emnlp)
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| Challenge: | In computational notebooks, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. |
| Approach: | They propose a new task of code documentation generation for computational notebooks that uses hierarchical attention mechanism to consider code cells and code tokens information when generating documentation. |
| Outcome: | The proposed model outperforms baseline models on a corpus constructed from well-documented Kaggle notebooks. |
A Multi-persona Framework for Argument Quality Assessment (2025.acl-long)
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| Challenge: | Existing methods for argument quality assessment do not consider multi-perspective evaluation due to subjective nature of arguments. |
| Approach: | They propose a multi-persona framework for argument quality assessment that simulates diverse evaluator perspectives through large language models. |
| Outcome: | The proposed framework outperforms baselines while providing comprehensive multi-perspective rationales on IBM-Rank-30k and IBM-ArgQ-5.3kArgs datasets. |
A Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization (2023.acl-long)
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Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Raghavi Chandu, João Sedoc
| Challenge: | Using crowdsourcing, it is difficult to obtain high-quality annotations for difficult tasks. |
| Approach: | They propose a recruitment pipeline to recruit high-quality Amazon Mechanical Turk workers . they filter out subpar workers before they carry out the evaluations . |
| Outcome: | The proposed method can filter out subpar workers before they carry out evaluations and obtain high-agreement annotations with similar constraints on resources. |
CiteBench: A Benchmark for Scientific Citation Text Generation (2023.emnlp-main)
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| Challenge: | Existing studies on citation text generation are based upon widely diverging task definitions, making it hard to study this task systematically. |
| Approach: | They propose a benchmark for citation text generation that unifies multiple datasets and enables standardized evaluation of citation texts across task designs and domains. |
| Outcome: | The proposed benchmark examines the performance of multiple strong baselines and enables standardized evaluation of citation text generation models across task designs and domains. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction (P19-1)
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| Challenge: | Recent years have witnessed a significant increase in laboratory-based evaluation benchmarks in many scientific disciplines. |
| Approach: | They propose to use NLP datasets to extract task, dataset, metric and score from NLP papers to build automatic leaderboards. |
| Outcome: | The proposed model outperforms baselines in the NLP domain by a large margin. |
End-to-end Neural Information Status Classification (2021.findings-emnlp)
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| Challenge: | Existing studies on information status classification and bridging anaphora recognition assume that gold mention or syntactic tree information is given. |
| Approach: | They propose an end-to-end neural approach for information status classification using a mention extraction component and an information status assignment component. |
| Outcome: | The proposed system achieves state-of-the-art on fine-grained IS classification based on gold mentions and better than baselines on ISNotes and SciCorp. |
AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis (2026.acl-demo)
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Massimiliano Pronesti, Angelo Miculescu, Mohsin Kapdi, Paul Flanagan, Oisín Redmond, Joao H Bettencourt-Silva, Gurdeep Singh Mannu, Spiros Denaxas, Rui Providencia, Anya Belz, Yufang Hou
| Challenge: | Existing systems that generate publication-ready forest plots from biomedical papers are fragmented and time-consuming. |
| Approach: | They propose a system that generates publication-ready forest plots directly from biomedical papers . autoforest automatically suggests ICO elements, extracts outcome data and performs statistical synthesis . authors demonstrate how the system can accelerate evidence synthesis and lower the barrier to conducting meta-analyses . |
| Outcome: | The proposed system accelerates evidence synthesis and lowers the barrier to meta-analyses. |
End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)
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| Challenge: | a new schema for NLP knowledge about tasks, datasets and metrics is proposed. |
| Approach: | They propose a new schema that represents knowledge about tasks, datasets and metrics in the NLP domain. |
| Outcome: | The proposed framework can be automatically built into scientific leaderboards . the proposed system achieves reasonable results for all relation types on this small-scale graph . |
SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement (2024.findings-emnlp)
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| Challenge: | Current text-to-image models struggle with generating accurate diagrams from long-context inputs. |
| Approach: | They propose a task that extracts relevant information from scientific papers and generates diagrams based on user intentions using intermediate code generation. |
| Outcome: | The proposed task outperforms existing models on factual correctness and visual appeal and outperfies existing ones on automatic and human judgement. |
Probing for Bridging Inference in Transformer Language Models (2021.naacl-main)
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| Challenge: | Pre-trained transformer language models are capable of bridging inference, but they lack the commonsense knowledge to capture syntactic information. |
| Approach: | They investigate whether pre-trained transformer language models capture bridging inference . they use a masked token prediction task to investigate attention heads in BERT . |
| Outcome: | The proposed model significantly captures bridging inference, the authors show . the distance between anaphor-antecedent and context plays an important role in the inference . |
Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization (2024.findings-eacl)
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| Challenge: | Pre-trained language models perform well in In-Topic setups, but face challenges in Cross-Topical setups where testing data is derived from distinct topics. |
| Approach: | They propose a probing-based approach to analyze pre-trained language models in a Cross-Topic setup to better understand the reasons behind generalization gaps. |
| Outcome: | The proposed approach improves on pre-trained language models in In-Topic setups and Cross-Topical scenarios. |
Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation (2022.emnlp-main)
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| Challenge: | Existing NLP task definitions for fact-checking cannot refute misinformation as professional fact- checkers do for the majority of claims. |
| Approach: | They compare NLP-based fact-checking with professional fact- checkers . they find that evidence must be sufficient to refute the claim and not leaked . |
| Outcome: | The proposed models fail to meet the criteria for realistic fact-checking . they also fail to satisfy the criteria of leaked evidence . |
MathBuddy: A Multimodal System for Affective Math Tutoring (2025.emnlp-demos)
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Debanjana Kar, Leopold Böss, Dacia Braca, Sebastian Maximilian Dennerlein, Nina Christine Hubig, Philipp Wintersberger, Yufang Hou
| Challenge: | Existing LLM-based conversational systems do not take into account the student’s affective states. |
| Approach: | They propose an emotionally aware LLM-powered math tutor that models student emotions and maps them to relevant pedagogical strategies. |
| Outcome: | The proposed model improves student engagement and learning effectiveness by 23 points using win rate and 3 points at an overall level using DAMR scores. |
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why? (2023.emnlp-main)
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| Challenge: | a systematic framework to analyze the evolution of research topics in a scientific field is crucial for keeping abreast of its continuous advancement. |
| Approach: | They propose a framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. |
| Outcome: | The proposed framework uncovers evolutionary trends and causes for a wide range of NLP topics. |
The Nature of NLP: Analyzing Contributions in NLP Papers (2025.acl-long)
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| Challenge: | despite this, what constitutes NLP research remains debated . |
| Approach: | They propose a taxonomy of research contributions and introduce a task of automatically identifying contribution statements and classifying their types from NLP research papers. |
| Outcome: | The proposed model analyzes 29k NLP research papers to understand their contributions . |
On the Role of Summary Content Units in Text Summarization Evaluation (2024.naacl-short)
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Marcel Nawrath, Agnieszka Nowak, Tristan Ratz, Danilo Walenta, Juri Opitz, Leonardo Ribeiro, João Sedoc, Daniel Deutsch, Simon Mille, Yixin Liu, Sebastian Gehrmann, Lining Zhang, Saad Mahamood, Miruna Clinciu, Khyathi Chandu, Yufang Hou
| Challenge: | a human written summary content unit (SCU) is used to judge the quality of a summary . a pyramid evaluation method is based on SCUs that decompose a reference summary into concise sentences . |
| Approach: | They propose to use automated SCUs to evaluate the quality of a candidate summary . they propose to generate SCU approximations from AMR meaning representations and large language models . |
| Outcome: | The proposed method can be fully automated, but lacks the human effort to validate it. |
PairSpanBERT: An Enhanced Language Model for Bridging Resolution (2023.acl-long)
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| Challenge: | bridging resolution is crucial for machine comprehension of discourse entities for various downstream applications. |
| Approach: | They propose a SpanBERT-based pre-trained model specialized for bridging resolution. |
| Outcome: | The proposed model achieves the best results on three evaluation datasets for bridging resolution despite the noise inherent in the automatically generated data . |
A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis (2026.findings-acl)
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Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, Umer Siddique, Zain Muhammad Mujahid, Yufang Hou, Preslav Nakov
| Challenge: | a large-scale label set for media outlets from Media Bias/Fact Check (MBFC) is lacking in the field. |
| Approach: | They propose to use a large-scale label set to analyze outlets' representations . they also propose to evaluate embedding views and fusion strategies . |
| Outcome: | The proposed method achieves state-of-the-art results on ACL-2020 and establishes strong benchmarks on MBFC-2025. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports (2020.lrec-1)
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Francesca Bonin, Martin Gleize, Ailbhe Finnerty, Candice Moore, Charles Jochim, Emma Norris, Yufang Hou, Alison J. Wright, Debasis Ganguly, Emily Hayes, Silje Zink, Alessandra Pascale, Pol Mac Aonghusa, Susan Michie
| Challenge: | Automated extraction of the reports’ intervention content, population, settings and their results is essential in synthesising and summarising the literature. |
| Approach: | They propose to build a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation and to release an annotation dataset. |
| Outcome: | The proposed corpus and annotation dataset are being made available to the community. |
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining (P18-2)
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Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
| Challenge: | Obtaining high quality labeled data for natural language understanding tasks is slow, error-prone, complicated and expensive. |
| Approach: | They propose a method to blend weak and strong labeled data during the training of neural networks using a topic-dependent evidence detection dataset. |
| Outcome: | The proposed method improves the training of neural networks when a small amount of labeled data is available. |
FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models (2026.acl-long)
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Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu, Tigran T. Tchrakian, James Barry, Jasmina Gajcin, Yufang Hou, Alessandra Pascale, Elizabeth M. Daly
| Challenge: | Large language models (LLMs) often produce factually incorrect responses. |
| Approach: | They propose a new method that adapts across domains without retraining and leverages structured feedback to generate a correction. |
| Outcome: | The proposed method outperforms baseline methods on a VELI5 dataset and several popular long-form factuality datasets. |
Beyond Outcome Verification: Verifiable Process Reward Models for Structured Reasoning (2026.findings-acl)
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| Challenge: | Recent work on reinforcement learning with verifiable rewards (RLVR) has shown that large language models can be substantially improved using outcome-level verification signals. |
| Approach: | They propose a framework where intermediate reasoning steps are checked by deterministic, rule-based verifiers. |
| Outcome: | The proposed framework achieves 20% higher F1 than state-of-the-art models and 6.5% higher than verifiable outcome rewards, with substantial gains in evidence grounding and logical coherence. |
A Deterministic Algorithm for Bridging Anaphora Resolution (D18-1)
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| Challenge: | Existing methods for bridging anaphora resolution only consider NPs’ head nouns and thus do not capture the semantics of NP. |
| Approach: | They propose a deterministic approach to bridging anaphora resolution which represents the semantics of an NP based on its head noun and modifications. |
| Outcome: | The proposed approach achieves competitive results compared to the best system in Hou et al. (2013) which explores Markov Logic Networks to model the problem. |
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022.acl-long)
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| Challenge: | Existing methods to generate educational questions of fairytales or storybooks are difficult to implement due to adults lacking the skills or time to integrate such interactive opportunities. |
| Approach: | They propose a question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. |
| Outcome: | The proposed method performs well on automatic and human evaluation metrics on a newly proposed educational question-answering dataset FairytaleQA. |
TaxoAlign: Scholarly Taxonomy Generation Using Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for taxonomy generation do not compare structure of generated surveys with those written by human experts. |
| Approach: | They propose a method that bridges the gap between human-generated and automatically-created taxonomies. |
| Outcome: | The proposed method surpasses baselines on CS-TaxoBench on nearly all metrics. |
Fine-grained Information Status Classification Using Discourse Context-Aware BERT (2020.coling-main)
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| Challenge: | Existing work on fine-grained information status (IS) relies on many hand-crafted linguistic features. |
| Approach: | They propose a discourse context-aware BERT model for fine-grained IS classification . they show an improvement of 10.5 F1 points for bridging anaphora recognition . |
| Outcome: | The proposed model achieves 4.8 absolute accuracy improvement on ISNotes corpus compared to previous work on bridging anaphora recognition . |
A Summarization System for Scientific Documents (D19-3)
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Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat, Ora Peled Nakash, Odellia Boni, Haggai Roitman, Doron Cohen, Bar Weiner, Yosi Mass, Or Rivlin, Guy Lev, Achiya Jerbi, Jonathan Herzig, Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Francesca Bonin, David Konopnicki
| Challenge: | a qualitative user study identified the most valuable scenarios for scientific content consumption. |
| Approach: | They propose a system that retrieves and summarizes scientific documents for a given information need. |
| Outcome: | The proposed system ingested 270,000 scientific papers and validated with human experts. |
Systematic Task Exploration with LLMs: A Study in Citation Text Generation (2024.acl-long)
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| Challenge: | Large language models (LLMs) provide unprecedented flexibility in defining and executing complex, creative natural language generation tasks. |
| Approach: | They propose a framework that consists of input manipulation, reference data, and output measurement to explore citation text generation. |
| Outcome: | The proposed framework explores citation text generation, a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric and has not yet been tackled within the LLM paradigm. |
Grounding Fallacies Misrepresenting Scientific Publications in Evidence (2025.naacl-long)
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| Challenge: | Current fact-checking models struggle to use misrepresented scientific passages to refute misinformation. |
| Approach: | They propose an extension of the fallacy detection dataset Missci to detect and highlight such fallacies by grounding applied fallacias in real-world passages from misrepresented studies. |
| Outcome: | The proposed model can detect and highlight misrepresented publications and verbalize them to refute them. |
End-to-End Neural Bridging Resolution (2022.coling-1)
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| Challenge: | state-of-the-art resolvers for bridging resolution are weaker than entity coreference resolution. |
| Approach: | They evaluate bridging resolvers in an end-to-end setting and strengthen them with better encoders . they also try to gain a better understanding of them through perturbation experiments . |
| Outcome: | bridging resolvers are evaluated in an end-to-end setting and strengthened with better encoders . bribridging resolution is the task of identifying briating anaphors and linking them to their antecedents - a paper by the journal bribing resolution argues . |
Bridging Anaphora Resolution as Question Answering (2020.acl-main)
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| Challenge: | Existing studies on bridging anaphora resolution focus on question answering based on context . briding anaphorisms and their antecedents are linked via various lexico-semantic, frame or encyclopedic relations. |
| Approach: | They propose a question answering framework for bridging anaphora resolution . they propose briding anaphorisms and their antecedents are linked via various lexico-semantic, frame or encyclopedic relations. |
| Outcome: | The proposed method generates state-of-the-art results on two bridging corpora. |
Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning (2025.emnlp-main)
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| Challenge: | Prior work has framed this task as a textual inference task by retrieving relevant content fragments and inferring conclusions from them. |
| Approach: | They propose to extract structured numerical evidence and apply domain knowledge informed logic to derive outcome-specific conclusions. |
| Outcome: | The proposed approach outperforms general-purpose LLMs of over 400B parameters and achieves a 21% improvement in F1 score over retrieval-based systems. |
FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models (2025.findings-emnlp)
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Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran T. Tchrakian, Javier Carnerero-Cano, Yufang Hou, Elizabeth M. Daly, Alessandra Pascale
| Challenge: | Large language models often fail to ensure factual accuracy of outputs thus limiting reliability in real-world applications. |
| Approach: | They propose a neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt-based methods in factual accuracy and recall. |
‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Automatic Science Journalism (2023.emnlp-main)
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| Challenge: | Science journalism is the production of journalistic content covering scientific topics that are not covered in the scientific literature. |
| Approach: | They propose to use a dataset to generate a scientific paper's tuples, a summary snippet and a novel technical framework to integrate a paper' s discourse structure with its metadata to guide generation. |
| Outcome: | The proposed system outperforms baseline methods in elaborating a content plan meaningful for the target audience, simplifying the information selected, and producing a coherent final report in a layman’s style. |
Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales (2023.acl-long)
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| Challenge: | Social biases and stereotypes are embedded in our culture through their presence in our stories. |
| Approach: | They propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. |
| Outcome: | The proposed framework extracts a story’s verb-based event chain for each of its characters as well as character attributes such as gender. |
Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
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| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
| Approach: | They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data. |
| Outcome: | The proposed model achieves state-of-the-art on three standard evaluation corpora. |
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics (2021.eacl-main)
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| Challenge: | Recent efforts to extract tasks, datasets and evaluation metrics from scientific literature focus on abstracts only. |
| Approach: | They propose a corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
| Outcome: | The proposed corpus contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
Enhanced Word Representations for Bridging Anaphora Resolution (N18-2)
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| Challenge: | Existing word representations do not capture semantic similarity for bridging anaphora resolution. |
| Approach: | They propose to use word embeddings to capture semantic similarity by exploring syntactic structure of noun phrases. |
| Outcome: | The proposed model achieves 30% of accuracy for bridging anaphora resolution on ISNotes corpus. |
Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards (2024.emnlp-main)
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| Challenge: | Existing leaderboards are incomplete and some contain incorrect information. |
| Approach: | They propose a manually-curated Scientific Leaderboard dataset that overcomes these problems . they propose three experimental settings where TDM triples are fully defined, partially defined, or undefined . |
| Outcome: | The proposed system overcomes the shortcomings of existing leaderboard datasets . it can be used to evaluate and compare scientific methods, but it requires manual labor . |