Search Results for author: Dirk Hovy

Found 78 papers, 15 papers with code

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

1 code implementation WASSA (ACL) 2022 Federico Bianchi, Debora Nozza, Dirk Hovy

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events.

Pipelines for Social Bias Testing of Large Language Models

no code implementations BigScience (ACL) 2022 Debora Nozza, Federico Bianchi, Dirk Hovy

We hope to open a discussion on the best methodologies to handle social bias testing in language models.

FEEL-IT: Emotion and Sentiment Classification for the Italian Language

1 code implementation EACL (WASSA) 2021 Federico Bianchi, Debora Nozza, Dirk Hovy

While sentiment analysis is a popular task to understand people’s reactions online, we often need more nuanced information: is the post negative because the user is angry or sad?

Classification Sentiment Analysis

We Need to Consider Disagreement in Evaluation

no code implementations ACL (BPPF) 2021 Valerio Basile, Michael Fell, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, Massimo Poesio, Alexandra Uma

Instead, we suggest that we need to better capture the sources of disagreement to improve today’s evaluation practice.

Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa

no code implementations ACL 2022 Tommaso Fornaciari, Alexandra Uma, Massimo Poesio, Dirk Hovy

Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models.

Experimental Design

A Report on the VarDial Evaluation Campaign 2020

no code implementations VarDial (COLING) 2020 Mihaela Gaman, Dirk Hovy, Radu Tudor Ionescu, Heidi Jauhiainen, Tommi Jauhiainen, Krister Lindén, Nikola Ljubešić, Niko Partanen, Christoph Purschke, Yves Scherrer, Marcos Zampieri

This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2020.

14 Dialect Identification

MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

no code implementations EACL (WASSA) 2021 Tommaso Fornaciari, Federico Bianchi, Debora Nozza, Dirk Hovy

The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification.

Emotion Classification Multi-Task Learning

Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists

1 code implementation Findings (ACL) 2022 Giuseppe Attanasio, Debora Nozza, Dirk Hovy, Elena Baralis

EAR also reveals overfitting terms, i. e., terms most likely to induce bias, to help identify their effect on the model, task, and predictions.

Bias Detection Fairness +2

Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

no code implementations24 Feb 2022 Anne Lauscher, Archie Crowley, Dirk Hovy

Based on our observations and ethical considerations, we define a series of desiderata for modeling pronouns in language technology.

Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

1 code implementation26 Jan 2022 Federico Bianchi, Vincenzo Cutrona, Dirk Hovy

Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years.

Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

1 code implementation14 Dec 2021 Paul Röttger, Bertie Vidgen, Dirk Hovy, Janet B. Pierrehumbert

To address this issue, we propose two contrasting paradigms for data annotation.

Language Invariant Properties in Natural Language Processing

1 code implementation nlppower (ACL) 2022 Federico Bianchi, Debora Nozza, Dirk Hovy

We introduce language invariant properties: i. e., properties that should not change when we transform text, and how they can be used to quantitatively evaluate the robustness of transformation algorithms.

Paraphrase Generation Translation

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling

no code implementations7 Jul 2021 Emily Dinan, Gavin Abercrombie, A. Stevie Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, Verena Rieser

Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans.

The Importance of Modeling Social Factors of Language: Theory and Practice

no code implementations NAACL 2021 Dirk Hovy, Diyi Yang

We show that current NLP systems systematically break down when faced with interpreting the social factors of language.

Integrating Ethics into the NLP Curriculum

no code implementations ACL 2020 Emily M. Bender, Dirk Hovy, Alex Schofield, ra

To raise awareness among future NLP practitioners and prevent inertia in the field, we need to place ethics in the curriculum for all NLP students{---}not as an elective, but as a core part of their education.

Cross-lingual Contextualized Topic Models with Zero-shot Learning

2 code implementations EACL 2021 Federico Bianchi, Silvia Terragni, Dirk Hovy, Debora Nozza, Elisabetta Fersini

They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.

Topic Models Transfer Learning +2

Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

3 code implementations ACL 2021 Federico Bianchi, Silvia Terragni, Dirk Hovy

Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data.

Sentence Embeddings Topic Models +1

What the [MASK]? Making Sense of Language-Specific BERT Models

no code implementations5 Mar 2020 Debora Nozza, Federico Bianchi, Dirk Hovy

Driven by the potential of BERT models, the NLP community has started to investigate and generate an abundant number of BERT models that are trained on a particular language, and tested on a specific data domain and task.

Language Modelling

Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

no code implementations WS 2019 Hanh Nguyen, Dirk Hovy

User reviews provide a significant source of information for companies to understand their market and audience.

Topic Models

Identifying Linguistic Areas for Geolocation

no code implementations WS 2019 Tommaso Fornaciari, Dirk Hovy

We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels.

Dense Node Representation for Geolocation

no code implementations WS 2019 Tommaso Fornaciari, Dirk Hovy

Prior research has shown that geolocation can be substantially improved by including user network information.

Geolocation with Attention-Based Multitask Learning Models

no code implementations WS 2019 Tommaso Fornaciari, Dirk Hovy

Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications.

Multi-class Classification

Women's Syntactic Resilience and Men's Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

no code implementations ACL 2019 Aparna Garimella, Carmen Banea, Dirk Hovy, Rada Mihalcea

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences.

Dependency Parsing Part-Of-Speech Tagging

Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information

1 code implementation EMNLP 2018 Dirk Hovy, Tommaso Fornaciari

We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter.

Classification General Classification +2

The Social and the Neural Network: How to Make Natural Language Processing about People again

no code implementations WS 2018 Dirk Hovy

Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the social aspects of language.

Comparing Bayesian Models of Annotation

no code implementations TACL 2018 Silviu Paun, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, Massimo Poesio

We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators.

Model Selection

Multi-Task Learning for Mental Health using Social Media Text

no code implementations10 Dec 2017 Adrian Benton, Margaret Mitchell, Dirk Hovy

We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework.

Gender Prediction Multi-Task Learning

Huntsville, hospitals, and hockey teams: Names can reveal your location

no code implementations WS 2017 Bahar Salehi, Dirk Hovy, Eduard Hovy, Anders S{\o}gaard

Geolocation is the task of identifying a social media user{'}s primary location, and in natural language processing, there is a growing literature on to what extent automated analysis of social media posts can help.

Knowledge Base Population Recommendation Systems +1

End-to-End Information Extraction without Token-Level Supervision

1 code implementation WS 2017 Rasmus Berg Palm, Dirk Hovy, Florian Laws, Ole Winther

End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels.

When POS data sets don't add up: Combatting sample bias

no code implementations LREC 2014 Dirk Hovy, Barbara Plank, Anders S{\o}gaard

We present a systematic study of several Twitter POS data sets, the problems of label and data bias, discuss their effects on model performance, and show how to overcome them to learn models that perform well on various test sets, achieving relative error reduction of up to 21{\%}.


Crowdsourcing and annotating NER for Twitter \#drift

no code implementations LREC 2014 Hege Fromreide, Dirk Hovy, Anders S{\o}gaard

We present two new NER datasets for Twitter; a manually annotated set of 1, 467 tweets (kappa=0. 942) and a set of 2, 975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in Finin et al. (2010).

Named Entity Recognition NER

Augmenting English Adjective Senses with Supersenses

1 code implementation LREC 2014 Yulia Tsvetkov, Nathan Schneider, Dirk Hovy, Archna Bhatia, Manaal Faruqui, Chris Dyer

We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification.

Classification General Classification

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