Papers by Ani Nenkova
Temporal Effects on Pre-trained Models for Language Processing Tasks (2022.tacl-1)
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| Challenge: | a recent study shows that language models can be improved as time passes . a number of approaches to solving language tasks have evolved rapidly without a model . |
| Approach: | They examine temporal effects on model performance on downstream language tasks . they also examine the efficacy of two approaches for temporal domain adaptation without human annotations . |
| Outcome: | The proposed methods improve self-labeling and named entity recognition on new data. |
Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling (2022.findings-emnlp)
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| Challenge: | Existing deep learning models for sequence labeling are expensive and time-consuming. |
| Approach: | They propose an interactive sequence labeling that allows training directly with the user feedback . they identify context and feedback biases by formulating interactive sequence labels via a Structural Causal Model. |
| Outcome: | The proposed approach can effectively alleviate the biases and can be learnt with the user feedback. |
LinkNav: Surfacing Interconnected Information in Scientific Articles (2026.acl-demo)
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| Challenge: | a non-linear reading order of academic literature is recognized by authors who make explicit connections between non-adjacent passages. |
| Approach: | They propose an enhanced reading experience which generates questions and searches for answer-bearing passages in academic papers to form intra-document connections when answers are found. |
| Outcome: | The proposed interface makes connections between related but non-adjacent passages even if the author did not make them explicit. |
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution (2021.eacl-main)
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| Challenge: | Existing tools for quantifying incivility online, in news and in congressional debates are inadequate for the analysis of incivility in news. |
| Approach: | They develop a Jigsaw Perspective API to quantify incivility in news . they show that toxicity models are inadequate for the analysis of incivility in news. |
| Outcome: | The Jigsaw Perspective API detects incivility on a corpus of American news articles. |
ATLAS: A System for PDF-centric Human Interaction Data Collection (2024.naacl-demo)
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| Challenge: | Recent advances in AI only make the importance of high-quality data more pronounced. |
| Approach: | They propose to use the Portable Document Format (PDF) as a data format to better support researchers in collecting rich PDF-centric datasets from users. |
| Outcome: | The proposed toolkit and extensible schema allows researchers to customize the data collection tasks for a variety of purposes, including annotations, drawing, and reading behavior analytics. |
Influence Functions for Sequence Tagging Models (2022.findings-emnlp)
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| Challenge: | Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling are standard tasks in NLP, but there has been little work on interpretability methods for sequence taging. |
| Approach: | They propose to extend influence functions to sequence tagging tasks by identifying noisy annotations in NER corpora. |
| Outcome: | The proposed methods are able to identify noisy annotations in NER corpora and are scalable. |
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction (N19-1)
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| Challenge: | Modern NLP systems require high-quality annotations, but experts are expensive and lay annotators may not have the knowledge to provide high- quality annotations. |
| Approach: | They propose to directly model instance difficulty to improve model performance and to route instances to appropriate annotators. |
| Outcome: | The proposed model improves performance on a biomedical information extraction task using expert and lay annotations. |
PDFTriage: Question Answering over Long, Structured Documents (2024.emnlp-industry)
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| Challenge: | Existing approaches to document QA use a pre-retrieval step to retrieve the relevant context from documents, but this is incongruous with the user's mental model of the document. |
| Approach: | They propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. |
| Outcome: | The proposed approach can retrieve context based on structure or content across several classes of questions where existing retrieval-augmented LLMs fail. |
Self-Repetition in Abstractive Neural Summarizers (2022.aacl-short)
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| Challenge: | a quantitative and qualitative analysis of self-repetition in neural summarization systems is needed to improve training. |
| Approach: | They propose a quantitative and qualitative analysis of self-repetition in neural summarization systems . they analyze the behavior of three popular architectures fine-tuned on five datasets . |
| Outcome: | The proposed model produces formulaic repetition in summaries, but not in summarization tasks. |
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)
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Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
| Challenge: | Existing prompt transfer techniques lack consideration for dialogue-specific information. |
| Approach: | They propose a method which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task. |
| Outcome: | The proposed method significantly outperforms baselines on two dialogue summarization benchmarks. |
Emotion Impacts Speech Recognition Performance (N19-3)
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| Challenge: | Existing studies show that speech recognition systems depend on multiple factors including lexical content, speaker identity and dialect. |
| Approach: | They propose a method that evaluates the impact of emotion on recognition even when manual transcripts are not available. |
| Outcome: | The proposed method allows to evaluate the impact of emotion on recognition even when manual transcripts are not available. |
Learning the Visualness of Text Using Large Vision-Language Models (2023.emnlp-main)
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| Challenge: | Visual text evokes an image in a person’s mind, while non-visual text fails to do so. |
| Approach: | They propose a method to automatically detect visualness in text to enable text-to-image retrieval and generation models to augment text with relevant images. |
| Outcome: | The proposed method performs better than several baseline models and heuristics for the task. |
DocTime: A Document-level Temporal Dependency Graph Parser (2022.naacl-main)
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Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, Rajiv Jain
| Challenge: | Document dependency graphs (TDGs) are used to understand the temporal relations between events mentioned in a document and to improve downstream tasks such as timeline creation and time-aware summarization. |
| Approach: | They propose a temporal dependency graph parser that takes input from a text document and produces a graph that incorporates longer range dependencies. |
| Outcome: | The proposed framework outperforms existing models on three datasets and improves tasks such as timeline creation, time-aware summarization, and temporal information extraction. |
Factual or Contextual? Disentangling Error Types in Entity Description Generation (2023.acl-long)
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| Challenge: | Existing evaluation practices only distinguish between model generated referring expressions being accurate (ground-truth) versus inaccurate (not groundtruth). |
| Approach: | They propose to integrate indicators for factual inconsistencies and contextual incongruities into automated evaluations of language models to assess the differences in error types across familiar vs unfamiliar entities. |
| Outcome: | The proposed evaluation paradigm disentangles factuality and congruity errors in natural contexts. |
Self-Cleaning: Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances (2024.findings-naacl)
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| Challenge: | Existing methods to train named entity recognition models on noisy data are expensive and time-intensive to accumulate. |
| Approach: | They propose to denoise noisy NER data with guidance from a small set of clean instances. |
| Outcome: | The proposed method can improve on large-scale datasets with a small guidance set. |
Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time (2020.acl-demos)
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| Challenge: | Trialstreamer extracts key pieces of information that clinicians need when appraising the literature . the highest-quality evidence to inform healthcare practice comes from randomized controlled trials . |
| Approach: | They propose a system that extracts key pieces of information from biomedical abstracts and combines them into a database of clinical trial reports. |
| Outcome: | The proposed system extracts descriptions of trial participants, treatments compared in each arm, and which outcomes were measured. |
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (P18-1)
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| Challenge: | In 2015 alone, about 100 manuscripts describing randomized controlled trials for medical interventions were published every day. |
| Approach: | They propose a corpus of 5,000 medical articles annotated with demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured. |
| Outcome: | The proposed corpus includes 5,000 medical articles describing clinical randomized controlled trials. |
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding (2022.emnlp-main)
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Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad Morariu
| Challenge: | Existing methods learn features from word-level or region-level but fail to consider both simultaneously. |
| Approach: | They propose a multi-modal multi-granular pre-training framework that encodes page-level, region-level and word-level information at the same time. |
| Outcome: | The proposed model learns features from word-level and region-level but fails to consider both simultaneously. |
Evaluating Multiple System Summary Lengths: A Case Study (D18-1)
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| Challenge: | Practical summarization systems are expected to produce summaries of varying lengths, per user needs. |
| Approach: | They propose to use ROUGE metric to evaluate system summaries of multiple lengths. |
| Outcome: | The evaluation protocol in question is competitive, the authors show . they found that the evaluation protocol is competitive with existing benchmarks. |
Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns (2022.findings-acl)
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Zihan Wang, Jiuxiang Gu, Jason Kuen, Handong Zhao, Vlad Morariu, Ruiyi Zhang, Ani Nenkova, Tong Sun, Jingbo Shang
| Challenge: | Adaptive Axis Attention learns different attention patterns for each task and model layer . sparse attention patterns do not improve the run time of the models but they reduce model memory requirements . |
| Approach: | They propose a method that learns different attention patterns for each Transformer layer . they propose 'adaptive axis attention' method that identifies important tokens . |
| Outcome: | The proposed method does not require pre-training to accommodate sparse attention patterns. |
The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization (D19-1)
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| Challenge: | Existing evaluation methods for summarization systems measure semantic overlap between a system summary and a human reference on word-string level. |
| Approach: | They propose to use distributed representations to evaluate system summary and human reference on word-string level. |
| Outcome: | The proposed representations outperform ROUGE on recent corpora but are less good on test data used in previous studies. |
Syntactic Patterns Improve Information Extraction for Medical Search (N18-2)
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| Challenge: | Medical professionals search the literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s). |
| Approach: | They propose to exploit the availability of structured abstracts to extract medically relevant information from syntactic patterns. |
| Outcome: | The proposed models differ from the constituent unigrams in the extracted patterns, suggesting that they capture contextual information that is otherwise lost. |
A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)
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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova
| Challenge: | Existing document understanding models focus on single-modal inputs such as images or texts. |
| Approach: | They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information. |
| Outcome: | The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines. |
SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps (2025.findings-emnlp)
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| Challenge: | SQLSpace is a representation for text-to-SQL examples derived with minimal human intervention. |
| Approach: | They introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples . they demonstrate that SQLSpace enables analysis that would be difficult with raw examples alone . |
| Outcome: | The proposed representations are human-interpretable and generalizable . they are used to evaluate models with a granularity beyond overall accuracy scores . |
The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English (2021.findings-acl)
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| Challenge: | Recent papers introduce methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models. |
| Approach: | They propose to integrate gazetteer features and entity segmentation techniques into neural named entity recognition models. |
| Outcome: | The proposed methods improve entity segmentation and not just entity typing. |
Named Entity Recognition in a Very Homogenous Domain (2023.findings-eacl)
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| Challenge: | Developing models that perform well on several domains is important, but domain is vague and can be adapted to a new domain. |
| Approach: | They find that even news articles from the same newspaper in English can be considered different domains. |
| Outcome: | The proposed model performs better on out-of-domain data than on specialized data. |