Search Results for author: Ani Nenkova

Found 57 papers, 7 papers with code

DocTime: A Document-level Temporal Dependency Graph Parser

no code implementations NAACL 2022 Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, Rajiv Jain

We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph.

Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances

no code implementations25 Oct 2023 Zhendong Chu, Ruiyi Zhang, Tong Yu, Rajiv Jain, Vlad I Morariu, Jiuxiang Gu, Ani Nenkova

To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate.


AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models

no code implementations23 Oct 2023 Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun

We can also customize the objective of \texttt{AutoDAN} to leak system prompts, beyond the ability to elicit harmful content from the model, demonstrating the versatility of the approach.

Adversarial Attack

PDFTriage: Question Answering over Long, Structured Documents

no code implementations16 Sep 2023 Jon Saad-Falcon, Joe Barrow, Alexa Siu, Ani Nenkova, David Seunghyun Yoon, Ryan A. Rossi, Franck Dernoncourt

Representing such structured documents as plain text is incongruous with the user's mental model of these documents with rich structure.

Question Answering Retrieval

Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness

no code implementations18 Jun 2023 David Demeter, Oshin Agarwal, Simon Ben Igeri, Marko Sterbentz, Neil Molino, John M. Conroy, Ani Nenkova

Academic literature does not give much guidance on how to build the best possible customer-facing summarization system from existing research components.

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer

no code implementations20 May 2023 Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl

In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.

Dialogue State Tracking Transfer Learning

MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding

no code implementations27 Nov 2022 Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad I. Morariu

In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.

Influence Functions for Sequence Tagging Models

1 code implementation25 Oct 2022 Sarthak Jain, Varun Manjunatha, Byron C. Wallace, Ani Nenkova

We show the practical utility of segment influence by using the method to identify systematic annotation errors in two named entity recognition corpora.

named-entity-recognition Named Entity Recognition +3

Self-Repetition in Abstractive Neural Summarizers

no code implementations14 Oct 2022 Nikita Salkar, Thomas Trikalinos, Byron C. Wallace, Ani Nenkova

In a regression analysis, we find that the three architectures have different propensities for repeating content across output summaries for inputs, with BART being particularly prone to self-repetition.

Temporal Effects on Pre-trained Models for Language Processing Tasks

1 code implementation24 Nov 2021 Oshin Agarwal, Ani Nenkova

Keeping the performance of language technologies optimal as time passes is of great practical interest.

Domain Adaptation Experimental Design +3

From Toxicity in Online Comments to Incivility in American News: Proceed with Caution

no code implementations EACL 2021 Anushree Hede, Oshin Agarwal, Linda Lu, Diana C. Mutz, Ani Nenkova

The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists.

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

no code implementations7 Oct 2020 Benjamin E. Nye, Jay DeYoung, Eric Lehman, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction).

Decision Making Relation Extraction

Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time

1 code implementation ACL 2020 Benjamin E. Nye, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic.

Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

no code implementations CL (ACL) 2021 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features.

named-entity-recognition Named Entity Recognition +1

Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models

1 code implementation8 Apr 2020 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.

Fairness named-entity-recognition +2

Emotion Impacts Speech Recognition Performance

no code implementations NAACL 2019 Rushab Munot, Ani Nenkova

It has been established that the performance of speech recognition systems depends on multiple factors including the lexical content, speaker identity and dialect.

speech-recognition Speech Recognition

Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence

no code implementations WS 2019 Soham Parikh, Elizabeth Conrad, Oshin Agarwal, Iain Marshall, Byron Wallace, Ani Nenkova

Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search.

Evaluation of named entity coreference

no code implementations WS 2019 Oshin Agarwal, Sanjay Subramanian, Ani Nenkova, Dan Roth

It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name.


How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature

no code implementations WS 2019 Simeng Sun, Ori Shapira, Ido Dagan, Ani Nenkova

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths.

Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction

no code implementations NAACL 2019 Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, Ani Nenkova

Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing.

Named Person Coreference in English News

no code implementations26 Oct 2018 Oshin Agarwal, Sanjay Subramanian, Ani Nenkova, Dan Roth

Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase.

coreference-resolution named-entity-recognition +2

Syntactic Patterns Improve Information Extraction for Medical Search

no code implementations NAACL 2018 Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron Wallace

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest.

Aggregating and Predicting Sequence Labels from Crowd Annotations

1 code implementation ACL 2017 An Thanh Nguyen, Byron Wallace, Junyi Jessy Li, Ani Nenkova, Matthew Lease

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text.

named-entity-recognition Named Entity Recognition +2

Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News

no code implementations3 Apr 2017 Yinfei Yang, Ani Nenkova

On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together.

Question Answering Test

Detecting (Un)Important Content for Single-Document News Summarization

no code implementations EACL 2017 Yinfei Yang, Forrest Sheng Bao, Ani Nenkova

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs.

Document Summarization News Summarization

Improving the Annotation of Sentence Specificity

no code implementations LREC 2016 Junyi Jessy Li, Bridget O{'}Daniel, Yi Wu, Wenli Zhao, Ani Nenkova

We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context.


What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain

no code implementations TACL 2013 Annie Louis, Ani Nenkova

We show that the distinction between great and typical articles can be detected fairly accurately, and that the entire spectrum of our features contribute to the distinction.

Information Retrieval Recommendation Systems

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