Search Results for author: Edison Marrese-Taylor

Found 24 papers, 12 papers with code

Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining

no code implementations NAACL (AmericasNLP) 2021 Francis Zheng, Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo

This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas.

Language Modelling Machine Translation +1

LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach

no code implementations19 Dec 2021 Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando, Hiroya Takamura, Qi Wu

We propose LocFormer, a Transformer-based model for video grounding which operates at a constant memory footprint regardless of the video length, i. e. number of frames.

Inductive Bias Video Grounding

Subformer: A Parameter Reduced Transformer

no code implementations1 Jan 2021 Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo

We also perform equally well as Transformer-big with 40% less parameters and outperform the model by 0. 7 BLEU with 12M less parameters.

Abstractive Text Summarization Language Modelling +2

DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video

1 code implementation13 Oct 2020 Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando, Hongdong Li, Stephen Gould

This paper studies the task of temporal moment localization in a long untrimmed video using natural language query.

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

no code implementations WS 2020 Edison Marrese-Taylor, Cristian Rodriguez-Opazo, Jorge A. Balazs, Stephen Gould, Yutaka Matsuo

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews.

Opinion Mining

Variational Inference for Learning Representations of Natural Language Edits

1 code implementation20 Apr 2020 Edison Marrese-Taylor, Machel Reid, Yutaka Matsuo

Document editing has become a pervasive component of the production of information, with version control systems enabling edits to be efficiently stored and applied.

Variational Inference

Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages

no code implementations9 Mar 2020 Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo

The contrast between the need for large amounts of data for current Natural Language Processing (NLP) techniques, and the lack thereof, is accentuated in the case of African languages, most of which are considered low-resource.

Translation Word Embeddings

An Edit-centric Approach for Wikipedia Article Quality Assessment

no code implementations WS 2019 Edison Marrese-Taylor, Pablo Loyola, Yutaka Matsuo

We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques.

Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention

1 code implementation20 Aug 2019 Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Fatemeh Sadat Saleh, Hongdong Li, Stephen Gould

Given an untrimmed video and a sentence as the query, the goal is to determine the starting, and the ending, of the relevant visual moment in the video, that corresponds to the query sentence.

Content Aware Source Code Change Description Generation

no code implementations WS 2018 Pablo Loyola, Edison Marrese-Taylor, Jorge Balazs, Yutaka Matsuo, Fumiko Satoh

We propose to study the generation of descriptions from source code changes by integrating the messages included on code commits and the intra-code documentation inside the source in the form of docstrings.

Machine Translation Text Generation

Deep contextualized word representations for detecting sarcasm and irony

1 code implementation WS 2018 Suzana Ilić, Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo

Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components.

Common Sense Reasoning

IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations

1 code implementation WS 2018 Jorge A. Balazs, Edison Marrese-Taylor, Yutaka Matsuo

In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2$^{\text{nd}}$ place out of 26 teams with a test macro F1 score of $0. 710$.

Emotion Classification General Classification

Learning to Automatically Generate Fill-In-The-Blank Quizzes

no code implementations WS 2018 Edison Marrese-Taylor, Ai Nakajima, Yutaka Matsuo, Ono Yuichi

In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each.

BIG-bench Machine Learning Question Generation +1

EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity

1 code implementation WS 2017 Edison Marrese-Taylor, Yutaka Matsuo

In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task.

Sentence Embedding Sentence-Embedding

Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN

1 code implementation WS 2017 Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo

These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and written text, which have been discussed extensively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.

Aspect Extraction Domain Adaptation +3

A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

1 code implementation ACL 2017 Pablo Loyola, Edison Marrese-Taylor, Yutaka Matsuo

We propose a model to automatically describe changes introduced in the source code of a program using natural language.

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