Search Results for author:
Found 30 papers, 15 papers with code
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining
This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas.
Improving Jejueo-Korean Translation With Cross-Lingual Pretraining Using Japanese and Korean
Jejueo is a critically endangered language spoken on Jeju Island and is closely related to but mutually unintelligible with Korean.
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain.
Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning.
Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language
The need for grounding in language understanding is an active research topic.
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
Our research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.
Target-Aware Contextual Political Bias Detection in News
Sentence-level political bias detection in news is no exception, and has proven to be a challenging task that requires an understanding of bias in consideration of the context.
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding
This paper explores the task of Temporal Video Grounding (TVG) where, given an untrimmed video and a natural language sentence query, the goal is to recognize and determine temporal boundaries of action instances in the video described by the query.
LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach
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.
Subformer: A Parameter Reduced Transformer
We also perform equally well as Transformer-big with 40% less parameters and outperform the model by 0. 7 BLEU with 12M less parameters.
Ranked #25 on
Machine Translation
on WMT2014 English-German
Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
In light of this, we explore parameter-sharing methods in Transformers with a specific focus on generative models.
DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video
This paper studies the task of temporal moment localization in a long untrimmed video using natural language query.
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling
In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases.
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews.
Variational Inference for Learning Representations of Natural Language Edits
Document editing has become a pervasive component of the production of information, with version control systems enabling edits to be efficiently stored and applied.
Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages
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.
An Edit-centric Approach for Wikipedia Article Quality Assessment
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
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
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.
Deep contextualized word representations for detecting sarcasm and irony
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.
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
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 $0. 710$.
Learning to Automatically Generate Fill-In-The-Blank Quizzes
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.
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018.
EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task.
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
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.
Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Finally it combines the refined representations of both sentences into a single vector to be used for classification.
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
We propose a model to automatically describe changes introduced in the source code of a program using natural language.
Replication issues in syntax-based aspect extraction for opinion mining
Reproducing experiments is an important instrument to validate previous work and build upon existing approaches.
