[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Sara Rosenthal


2024

pdf bib
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
Atul Kr. Ojha | A. Seza Doğruöz | Harish Tayyar Madabushi | Giovanni Da San Martino | Sara Rosenthal | Aiala Rosá
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

2023

pdf bib
Target-Based Offensive Language Identification
Marcos Zampieri | Skye Morgan | Kai North | Tharindu Ranasinghe | Austin Simmmons | Paridhi Khandelwal | Sara Rosenthal | Preslav Nakov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on English Twitter posts. We release an initial dataset of over 4,500 instances collected from Twitter and we carry out multiple experiments to compare the performance of different models trained and tested on TBO.

pdf bib
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
Avi Sil | Jaydeep Sen | Bhavani Iyer | Martin Franz | Kshitij Fadnis | Mihaela Bornea | Sara Rosenthal | Scott McCarley | Rong Zhang | Vishwajeet Kumar | Yulong Li | Md Arafat Sultan | Riyaz Bhat | Juergen Bross | Radu Florian | Salim Roukos
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers. In this paper, we introduce PrimeQA: a one-stop and open-source QA repository with an aim to democratize QA research and facilitate easy replication of state-of-the-art (SOTA) QA methods. PrimeQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question generation. It has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on public benchmarks, and expanding pre-existing methods. PrimeQA is available at: https://github.com/primeqa.

pdf bib
Muted: Multilingual Targeted Offensive Speech Identification and Visualization
Christoph Tillmann | Aashka Trivedi | Sara Rosenthal | Santosh Borse | Rong Zhang | Avirup Sil | Bishwaranjan Bhattacharjee
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web. While previous work has mostly dealt with sentence level annotations, there have been a few recent attempts to identify offensive spans as well. We build upon this work and introduce MUTED, a system to identify multilingual HAP content by displaying offensive arguments and their targets using heat maps to indicate their intensity. MUTED can leverage any transformer-based HAP-classification model and its attention mechanism out-of-the-box to identify toxic spans, without further fine-tuning. In addition, we use the spaCy library to identify the specific targets and arguments for the words predicted by the attention heatmaps. We present the model’s performance on identifying offensive spans and their targets in existing datasets and present new annotations on German text. Finally, we demonstrate our proposed visualization tool on multilingual inputs.

pdf bib
Towards Effective Long-Form QA with Evidence Augmentation
Mengxia Yu | Sara Rosenthal | Mihaela Bornea | Avi Sil
Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

In this study, we focus on the challenge of improving Long-form Question Answering (LFQA) by extracting and effectively utilizing knowledge from a large set of retrieved passages. We first demonstrate the importance of accurate evidence retrieval for LFQA, showing that optimal extracted knowledge from passages significantly benefits the generation. We also show that the choice of generative models impacts the system’s ability to leverage the evidence and produce answers that are grounded in the retrieved passages. We propose a Mixture of Experts (MoE) model as an alternative to the Fusion in Decoder (FiD) used in state-of-the-art LFQA systems and we compare these two models in our experiments.

2022

pdf bib
Task Transfer and Domain Adaptation for Zero-Shot Question Answering
Xiang Pan | Alex Sheng | David Shimshoni | Aditya Singhal | Sara Rosenthal | Avirup Sil
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be available. To address this, we use supervised pretraining on source-domain data to reduce sample complexity on domainspecific downstream tasks. We evaluate zeroshot performance on domain-specific reading comprehension tasks by combining task transfer with domain adaptation to fine-tune a pretrained model with no labelled data from the target task. Our approach outperforms DomainAdaptive Pretraining on downstream domainspecific reading comprehension tasks in 3 out of 4 domains.

2021

pdf bib
SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)
Nancy X. R. Wang | Diwakar Mahajan | Marina Danilevsky | Sara Rosenthal
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.

pdf bib
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification
Sara Rosenthal | Pepa Atanasova | Georgi Karadzhov | Marcos Zampieri | Preslav Nakov
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

pdf bib
A Multilingual Reading Comprehension System for more than 100 Languages
Anthony Ferritto | Sara Rosenthal | Mihaela Bornea | Kazi Hasan | Rishav Chakravarti | Salim Roukos | Radu Florian | Avi Sil
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

This paper presents M-GAAMA, a Multilingual Question Answering architecture and demo system. This is the first multilingual machine reading comprehension (MRC) demo which is able to answer questions in over 100 languages. M-GAAMA answers questions from a given passage in the same or different language. It incorporates several existing multilingual models that can be used interchangeably in the demo such as M-BERT and XLM-R. The M-GAAMA demo also improves language accessibility by incorporating the IBM Watson machine translation widget to provide additional capabilities to the user to see an answer in their desired language. We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019). We experiment with our system architecture on the Multi-Lingual Question Answering (MLQA) and the COVID-19 CORD (Wang et al., 2020; Tang et al., 2020) datasets to provide insights into the performance of the system.

pdf bib
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
Marcos Zampieri | Preslav Nakov | Sara Rosenthal | Pepa Atanasova | Georgi Karadzhov | Hamdy Mubarak | Leon Derczynski | Zeses Pitenis | Çağrı Çöltekin
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.

2019

pdf bib
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Sara Rosenthal | Noura Farra | Ritesh Kumar
Proceedings of the 13th International Workshop on Semantic Evaluation

We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.

pdf bib
Predicting the Type and Target of Offensive Posts in Social Media
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Sara Rosenthal | Noura Farra | Ritesh Kumar
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.

pdf bib
Leveraging Medical Literature for Section Prediction in Electronic Health Records
Sara Rosenthal | Ken Barker | Zhicheng Liang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Electronic Health Records (EHRs) contain both structured content and unstructured (text) content about a patient’s medical history. In the unstructured text parts, there are common sections such as Assessment and Plan, Social History, and Medications. These sections help physicians find information easily and can be used by an information retrieval system to return specific information sought by a user. However, it is common that the exact format of sections in a particular EHR does not adhere to known patterns. Therefore, being able to predict sections and headers in EHRs automatically is beneficial to physicians. Prior approaches in EHR section prediction have only used text data from EHRs and have required significant manual annotation. We propose using sections from medical literature (e.g., textbooks, journals, web content) that contain content similar to that found in EHR sections. Our approach uses data from a different kind of source where labels are provided without the need of a time-consuming annotation effort. We use this data to train two models: an RNN and a BERT-based model. We apply the learned models along with source data via transfer learning to predict sections in EHRs. Our results show that medical literature can provide helpful supervision signal for this classification task.

2018

pdf bib
Toward Cross-Domain Engagement Analysis in Medical Notes
Sara Rosenthal | Adam Faulkner
Proceedings of the BioNLP 2018 workshop

We present a novel annotation task evaluating a patient’s engagement with their health care regimen. The concept of engagement supplements the traditional concept of adherence with a focus on the patient’s affect, lifestyle choices, and health goal status. We describe an engagement annotation task across two patient note domains: traditional clinical notes and a novel domain, care manager notes, where we find engagement to be more common. The annotation task resulted in a kappa of .53, suggesting strong annotator intuitions regarding engagement-bearing language. In addition, we report the results of a series of preliminary engagement classification experiments using domain adaptation.

2017

pdf bib
SemEval-2017 Task 4: Sentiment Analysis in Twitter
Sara Rosenthal | Noura Farra | Preslav Nakov
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

2016

pdf bib
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov | Alan Ritter | Sara Rosenthal | Fabrizio Sebastiani | Veselin Stoyanov
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

pdf bib
Social Proof: The Impact of Author Traits on Influence Detection
Sara Rosenthal | Kathy McKeown
Proceedings of the First Workshop on NLP and Computational Social Science

2015

pdf bib
SemEval-2015 Task 10: Sentiment Analysis in Twitter
Sara Rosenthal | Preslav Nakov | Svetlana Kiritchenko | Saif Mohammad | Alan Ritter | Veselin Stoyanov
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

pdf bib
I Couldn’t Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions
Sara Rosenthal | Kathy McKeown
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

pdf bib
SemEval-2014 Task 9: Sentiment Analysis in Twitter
Sara Rosenthal | Alan Ritter | Preslav Nakov | Veselin Stoyanov
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media
Sara Rosenthal | Kathy McKeown | Apoorv Agarwal
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

pdf bib
SemEval-2013 Task 2: Sentiment Analysis in Twitter
Preslav Nakov | Sara Rosenthal | Zornitsa Kozareva | Veselin Stoyanov | Alan Ritter | Theresa Wilson
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

pdf bib
Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media
Sara Rosenthal | Kathy McKeown
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

pdf bib
Detecting Influencers in Written Online Conversations
Or Biran | Sara Rosenthal | Jacob Andreas | Kathleen McKeown | Owen Rambow
Proceedings of the Second Workshop on Language in Social Media

pdf bib
Annotating Agreement and Disagreement in Threaded Discussion
Jacob Andreas | Sara Rosenthal | Kathleen McKeown
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We introduce a new corpus of sentence-level agreement and disagreement annotations over LiveJournal and Wikipedia threads. This is the first agreement corpus to offer full-document annotations for threaded discussions. We provide a methodology for coding responses as well as an implemented tool with an interface that facilitates annotation of a specific response while viewing the full context of the thread. Both the results of an annotator questionnaire and high inter-annotator agreement statistics indicate that the annotations collected are of high quality.

2011

pdf bib
Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations
Sara Rosenthal | Kathleen McKeown
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

pdf bib
Time-Efficient Creation of an Accurate Sentence Fusion Corpus
Kathleen McKeown | Sara Rosenthal | Kapil Thadani | Coleman Moore
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

pdf bib
Corpus Creation for New Genres: A Crowdsourced Approach to PP Attachment
Mukund Jha | Jacob Andreas | Kapil Thadani | Sara Rosenthal | Kathleen McKeown
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

pdf bib
Towards Semi-Automated Annotation for Prepositional Phrase Attachment
Sara Rosenthal | William Lipovsky | Kathleen McKeown | Kapil Thadani | Jacob Andreas
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper investigates whether high-quality annotations for tasks involving semantic disambiguation can be obtained without a major investment in time or expense. We examine the use of untrained human volunteers from Amazons Mechanical Turk in disambiguating prepositional phrase (PP) attachment over sentences drawn from the Wall Street Journal corpus. Our goal is to compare the performance of these crowdsourced judgments to the annotations supplied by trained linguists for the Penn Treebank project in order to indicate the viability of this approach for annotation projects that involve contextual disambiguation. The results of our experiments on a sample of the Wall Street Journal corpus show that invoking majority agreement between multiple human workers can yield PP attachments with fairly high precision. This confirms that a crowdsourcing approach to syntactic annotation holds promise for the generation of training corpora in new domains and genres where high-quality annotations are not available and difficult to obtain.