Search Results for author: Rebecca J. Passonneau

Found 24 papers, 6 papers with code

Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction

1 code implementation NAACL (TextGraphs) 2021 Yanjun Gao, Ting-Hao Huang, Rebecca J. Passonneau

We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase.

Discourse Parsing Graph Learning

Contrastive Data and Learning for Natural Language Processing

no code implementations NAACL (ACL) 2022 Rui Zhang, Yangfeng Ji, Yue Zhang, Rebecca J. Passonneau

We then survey the benefits and the best practices of contrastive learning for various downstream NLP applications including Text Classification, Question Answering, Summarization, Text Generation, Interpretability and Explainability, Commonsense Knowledge and Reasoning, Vision-and-Language. This tutorial intends to help researchers in the NLP and computational linguistics community to understand this emerging topic and promote future research directions of using contrastive learning for NLP applications.

Contrastive Learning Question Answering +4

The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis

no code implementations18 Oct 2023 Pranav Narayanan Venkit, Mukund Srinath, Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca J. Passonneau, Shomir Wilson

We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets.

Ethics Sentiment Analysis

CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias

1 code implementation24 Aug 2023 Vipul Gupta, Pranav Narayanan Venkit, Hugo Laurençon, Shomir Wilson, Rebecca J. Passonneau

To achieve reliability, we introduce the Comprehensive Assessment of Language Model bias (CALM), a benchmark dataset to quantify bias in LMs across three tasks.

Language Modelling Semantic Similarity +1

Survey on Sociodemographic Bias in Natural Language Processing

no code implementations13 Jun 2023 Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, Rebecca J. Passonneau

In this study, we aim to provide a more comprehensive understanding of the similarities and differences among approaches to sociodemographic bias in NLP.

ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

2 code implementations ACL 2021 Yanjun Gao, Ting-Hao, Huang, Rebecca J. Passonneau

On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.

Argument Mining Discourse Parsing +2

Automated Pyramid Summarization Evaluation

1 code implementation CONLL 2019 Yanjun Gao, Chen Sun, Rebecca J. Passonneau

Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts.

Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays

1 code implementation WS 2019 Yanjun Gao, Alex Driban, Brennan Xavier McManus, Elena Musi, Patricia Davies, Smar Muresan, a, Rebecca J. Passonneau

We present a unique dataset of student source-based argument essays to facilitate research on the relations between content, argumentation skills, and assessment.

OmniGraph: Rich Representation and Graph Kernel Learning

no code implementations10 Oct 2015 Boyi Xie, Rebecca J. Passonneau

OmniGraph, a novel representation to support a range of NLP classification tasks, integrates lexical items, syntactic dependencies and frame semantic parses into graphs.

Feature Engineering General Classification

Abstractive Multi-Document Summarization via Phrase Selection and Merging

no code implementations IJCNLP 2015 Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, Rebecca J. Passonneau

We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases.

Document Summarization Multi-Document Summarization

Annotating the MASC Corpus with BabelNet

no code implementations LREC 2014 Andrea Moro, Roberto Navigli, Francesco Maria Tucci, Rebecca J. Passonneau

Finally, we estimate the quality of our annotations using both manually-tagged named entities and word senses, obtaining an accuracy of roughly 70{\%} for both named entities and word sense annotations.

Entity Linking Reading Comprehension +2

The Benefits of a Model of Annotation

no code implementations TACL 2014 Rebecca J. Passonneau, Bob Carpenter

Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus.


Empirical Comparisons of MASC Word Sense Annotations

no code implementations LREC 2012 Gerard de Melo, Collin F. Baker, Nancy Ide, Rebecca J. Passonneau, Christiane Fellbaum

We analyze how different conceptions of lexical semantics affect sense annotations and how multiple sense inventories can be compared empirically, based on annotated text.

The MASC Word Sense Corpus

no code implementations LREC 2012 Rebecca J. Passonneau, Collin F. Baker, Christiane Fellbaum, Nancy Ide

The MASC project has produced a multi-genre corpus with multiple layers of linguistic annotation, together with a sentence corpus containing WordNet 3. 1 sense tags for 1000 occurrences of each of 100 words produced by multiple annotators, accompanied by indepth inter-annotator agreement data.

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