Search Results for author: Leonardo Neves

Found 18 papers, 10 papers with code

Efficient Learning of Less Biased Models with Transfer Learning

no code implementations1 Jan 2021 Xisen Jin, Francesco Barbieri, Leonardo Neves, Xiang Ren

Prediction bias in machine learning models, referring to undesirable model behaviors that discriminates inputs mentioning or produced by certain group, has drawn increasing attention from the research community given its societal impact.

Transfer Learning

The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks

1 code implementation COLING 2020 Brihi Joshi, Neil Shah, Francesco Barbieri, Leonardo Neves

Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years.

Question Answering Sentiment Analysis

On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning

no code implementations NAACL 2021 Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, Xiang Ren

Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution.

Coreference Resolution Fairness +5

Data Augmentation for Graph Neural Networks

1 code implementation11 Jun 2020 Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah

Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction.

Data Augmentation General Classification +1

Learning from Explanations with Neural Execution Tree

1 code implementation ICLR 2020 Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, Xiang Ren

While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive.

Data Augmentation Multi-hop Question Answering +5

NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction

2 code implementations5 Sep 2019 Wenxuan Zhou, Hongtao Lin, Bill Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, Xiang Ren

The soft matching module learns to match rules with semantically similar sentences such that raw corpora can be automatically labeled and leveraged by the RE module (in a much better coverage) as augmented supervision, in addition to the exactly matched sentences.

Relation Extraction

Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering

no code implementations NAACL 2019 Lahari Poddar, Leonardo Neves, William Brendel, Luis Marujo, Sergey Tulyakov, Pradeep Karuturi

Leveraging the assumption that learning the topic of a bug is a sub-task for detecting duplicates, we design a loss function that can jointly perform both tasks but needs supervision for only duplicate classification, achieving topic clustering in an unsupervised fashion.

General Classification

Multimodal Named Entity Disambiguation for Noisy Social Media Posts

no code implementations ACL 2018 Seungwhan Moon, Leonardo Neves, Vitor Carvalho

We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images.

Entity Disambiguation Image Captioning +2

Visual Attention Model for Name Tagging in Multimodal Social Media

no code implementations ACL 2018 Di Lu, Leonardo Neves, Vitor Carvalho, Ning Zhang, Heng Ji

Everyday billions of multimodal posts containing both images and text are shared in social media sites such as Snapchat, Twitter or Instagram.

Natural Language Understanding Question Answering

Multimodal Named Entity Recognition for Short Social Media Posts

no code implementations NAACL 2018 Seungwhan Moon, Leonardo Neves, Vitor Carvalho

We introduce a new task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data such as tweets or Snapchat captions, which comprise short text with accompanying images.

named-entity-recognition NER

Visual Features for Context-Aware Speech Recognition

no code implementations1 Dec 2017 Abhinav Gupta, Yajie Miao, Leonardo Neves, Florian Metze

We are working on a corpus of "how-to" videos from the web, and the idea is that an object that can be seen ("car"), or a scene that is being detected ("kitchen") can be used to condition both models on the "context" of the recording, thereby reducing perplexity and improving transcription.

speech-recognition Speech Recognition

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