Search Results for author: Cicero Nogueira dos santos

Found 29 papers, 10 papers with code

H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network

no code implementations EMNLP 2020 Shen Wang, Xiaokai Wei, Cicero Nogueira dos santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu

Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.

Graph Attention Knowledge Graph Embedding +2

Contrastive Fine-tuning Improves Robustness for Neural Rankers

no code implementations Findings (ACL) 2021 Xiaofei Ma, Cicero Nogueira dos santos, Andrew O. Arnold

The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain.

Data Augmentation Passage Ranking

Joint Text and Label Generation for Spoken Language Understanding

no code implementations11 May 2021 Yang Li, Ben Athiwaratkun, Cicero Nogueira dos santos, Bing Xiang

In this work, we propose to leverage the prior information embedded in pretrained language models (LM) to improve generalization for intent classification and slot labeling tasks with limited training data.

Intent Classification Learning with noisy labels +2

Structured Prediction as Translation between Augmented Natural Languages

1 code implementation ICLR 2021 Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos santos, Bing Xiang, Stefano Soatto

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.

Coreference Resolution Dialogue State Tracking +9

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

3 code implementations18 Dec 2020 Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos santos, Bing Xiang

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).

Language Modelling Self-Supervised Learning +2

DualTKB: A Dual Learning Bridge between Text and Knowledge Base

no code implementations EMNLP 2020 Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos santos, Payel Das

In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).

Beyond [CLS] through Ranking by Generation

no code implementations EMNLP 2020 Cicero Nogueira dos santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in various IR tasks in the past.

Answer Selection Information Retrieval +3

Embedding-based Zero-shot Retrieval through Query Generation

1 code implementation22 Sep 2020 Davis Liang, Peng Xu, Siamak Shakeri, Cicero Nogueira dos Santos, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

In some cases, our model trained on synthetic data can even outperform the same model trained on real data

Passage Retrieval

Augmented Natural Language for Generative Sequence Labeling

no code implementations EMNLP 2020 Ben Athiwaratkun, Cicero Nogueira dos santos, Jason Krone, Bing Xiang

We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot ($75. 0\% \rightarrow 90. 9\%$) and 1-shot ($70. 4\% \rightarrow 81. 0\%$) state-of-the-art results.

Intent Classification Named Entity Recognition

Learning Implicit Text Generation via Feature Matching

no code implementations ACL 2020 Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.

Conditional Text Generation Style Transfer +2

Covering the News with (AI) Style

no code implementations5 Jan 2020 Michele Merler, Cicero Nogueira dos santos, Mauro Martino, Alfio M. Gliozzo, John R. Smith

We introduce a multi-modal discriminative and generative frame-work capable of assisting humans in producing visual content re-lated to a given theme, starting from a collection of documents(textual, visual, or both).


Generative Feature Matching Networks

no code implementations ICLR 2019 Cicero Nogueira dos Santos, Inkit Padhi, Pierre Dognin, Youssef Mroueh

We propose a non-adversarial feature matching-based approach to train generative models.

Learning Implicit Generative Models by Matching Perceptual Features

no code implementations ICCV 2019 Cicero Nogueira dos Santos, Youssef Mroueh, Inkit Padhi, Pierre Dognin

Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution.

online learning Style Transfer +2

Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer

no code implementations ACL 2018 Cicero Nogueira dos Santos, Igor Melnyk, Inkit Padhi

We introduce a new approach to tackle the problem of offensive language in online social media.

Style Transfer Text Style Transfer +1

Improved Neural Text Attribute Transfer with Non-parallel Data

no code implementations26 Nov 2017 Igor Melnyk, Cicero Nogueira dos santos, Kahini Wadhawan, Inkit Padhi, Abhishek Kumar

Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes.

Disentanglement Text Attribute Transfer

Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks

no code implementations7 Jul 2017 Cicero Nogueira dos Santos, Kahini Wadhawan, Bo-Wen Zhou

We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning.

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

4 code implementations CONLL 2016 Ramesh Nallapati, Bo-Wen Zhou, Cicero Nogueira dos santos, Caglar Gulcehre, Bing Xiang

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.

Abstractive Text Summarization Sentence Summarization +1

Boosting Named Entity Recognition with Neural Character Embeddings

no code implementations WS 2015 Cicero Nogueira dos Santos, Victor Guimarães

Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking.

Chunking Named Entity Recognition +3

Classifying Relations by Ranking with Convolutional Neural Networks

2 code implementations IJCNLP 2015 Cicero Nogueira dos Santos, Bing Xiang, Bo-Wen Zhou

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.

Classification General Classification +2

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